ATRIP: An MPI-asynchronous implementation of CCSD(T)¶
1 Introduction¶
The algorithm uses two main data types, the Slice and the
SliceUnion as a container and resource manager of the Slice.
2 The slice¶
The following section introduces the idea of a slice.
2.1 Introduction¶
A slice is the concept of a subset of values of a given tensor. As an example, for the doubles amplitudes \(T^{ab}_{ij}\), one need two kinds of objects:
the object \(\mathsf{T}(a)^b_{ij}\) which for every \(a\) gets assigned the tensor \(T^{ab}_{ij}\) of size \(N_\mathrm{o}^2 \times N_\mathrm{v}\)
the object \(\mathsf{T}(a,b)_{ij}\) which for every pair of \(a, b\) corresponds the \(N_\mathrm{o}^2\)-sized tensor \(T^{ab}_{ij}\).
2.2 Location¶
Every slice set, for instance,
\[S_k = \left\{
a \mapsto \mathsf{T}(a)^{b}_{ij}
\mid
a \in A_k
\right\}\]
where \(A_k\) is some subset of
\(\mathsf{N}_\mathrm{v}\),
gets stored in some rank \(k\).
In general however, the number of elements in \(A_k\) can be bigger
than the number of processes \(n_p\). Therefore in order to uniquely
indentify a given slice in \(S_k\) we need two identifiers,
the rank \(k\), which tells us in which core’s memory the slice is
allocated, and an additional tag which we will call source.
The datatype that simply models this state of affairs is therefore a simple structure:
struct Location { size_t rank; size_t source; };
2.3 Type¶
Due to the permutation operators in the equations it is noticeable that for every one dimensional slice and triple \((a,b,c)\)
\[a \mapsto \mathsf{t}(a)\]
one needs at the same time \(\mathsf{t}(a)\), \(\mathsf{t}(b)\) and \(\mathsf{t}(c)\). For two dimensional slices, i.e., slices of the form
\[(a,b) \mapsto \mathsf{t}(a,b)\]
one needs in the equations the slices \(\mathsf{t}(a,b)\), \(\mathsf{t}(b,c)\) and \(\mathsf{t}(a,c)\). In addition, in the case of diagrams where the integral \(V^{ab}_{ci}\) appears, we additionaly need the permuted slices from before, i.e. \(\mathsf{t}(b,a)\), \(\mathsf{t}(c,b)\) and \(\mathsf{t}(c,a)\).
This means, every slice has associated with it a type which denotes which permutation it is.
enum Type
{ A = 10
, B
, C
// Two-parameter slices
, AB = 20
, BC
, AC
// for abci and the doubles
, CB
, BA
, CA
// The non-typed slice
, Blank = 404
};
2.4 State¶
Every slice can be in different states and every state denotes which function the slice is going to provide and which relations they have between themselves.
- Fetch
A slice is in state
Fetchwhen it has a valid data pointer that **must** be written to. AFetchslice should not live very long, this means that after the database send and receive phase,Fetchslices should be changed intoDispatchedin order to start the process of writing to the data pointer from some other rank.- Dispatched
A
Dispatchedslice indicates that at some point send and receive MPI calls have been dispatched in order to get the data. However, the calls have just been dispatched and there is no warranty for the data to be there, for that, the slice must be unwrapped.- Ready
Readymeans that the data pointer can be read from directly.- SelfSufficient
A slice is
SelfSufficientwhen its contents are located in the same rank that it lives, so that it does not have to fetch from no other rank. This is important in order to handle the data pointers correctly and in order to save calls to MPI receive and send functions.- Recycled
Recycledmeans that this slice gets its data pointer from another slice, so it should not be written to- Acceptor
Acceptormeans that the slice can accept a new slice, it is the counterpart of theBlanktype, but for states
Again the implementation is a simple enum type.
enum State {
Fetch = 0,
Dispatched = 2,
Ready = 1,
SelfSufficient = 911,
Recycled = 123,
Acceptor = 405
};
2.5 Data pointer¶
The data pointer type is an abstraction of where the data
of the slice are.
In a CPU architecture it will simply be F*,
otherwise it will be a pointer to a GPU address in the case
of a GPU architecture.
#pragma once
#include <atrip/Complex.hpp>
#include <atrip/Atrip.hpp>
namespace atrip {
template <typename F>
struct DataField;
template <>
struct DataField<double> {
using type = double;
};
#if defined(HAVE_CUDA)
template <typename F>
using DataPtr = CUdeviceptr;
#define DataNullPtr 0x00
template <>
struct DataField<Complex> {
using type = cuDoubleComplex;
};
#else
template <typename F>
using DataPtr = F*;
#define DataNullPtr nullptr
template <>
struct DataField<Complex> {
using type = Complex;
};
#endif
template <typename F>
using DataFieldType = typename DataField<F>::type;
}
2.6 The Info structure¶
Every slice has an information structure associated with it that keeps track of the **variable** type, state and so on.
struct Info {
// which part of a,b,c the slice holds
PartialTuple tuple;
// The type of slice for the user to retrieve the correct one
Type type;
// What is the state of the slice
State state;
// Where the slice is to be retrieved
Location from;
// If the data are actually to be found in this other slice
Type recycling;
Info() : tuple{0,0}
, type{Blank}
, state{Acceptor}
, from{0,0}
, recycling{Blank}
{}
};
using Ty_x_Tu = std::pair< Type, PartialTuple >;
2.7 Name¶
CCSD(T) needs in this algorithm 5 types of tensor slices, namely \(V^{ij}_{ka}\), \(V^{ab}_{ci}\), \(V^{ab}_{ij}\) and two times \(T^{ab}_{ij}\). The reason why we need two times the doubles amplitudes is because in the doubles contribution to the energy, the \(T\) amplidutes will be sliced through one parameter for the particle contribution and through two parameters for the hole contribution.
enum Name
{ TA = 100
, VIJKA = 101
, VABCI = 200
, TABIJ = 201
, VABIJ = 202
};
2.8 Database¶
The database is a simple representation of the slices of a slice union. Every element of the database is given by the name of the tensor it represents and the internal information structure.
struct LocalDatabaseElement {
Slice<F>::Name name;
Slice<F>::Info info;
};
A local database (of a given rank) and the global database is thus simply a vector of these elements.
using LocalDatabase = std::vector<LocalDatabaseElement>;
using Database = LocalDatabase;
2.9 MPI Types¶
struct mpi {
static MPI_Datatype vector(size_t n, MPI_Datatype const& DT) {
MPI_Datatype dt;
MPI_Type_vector(n, 1, 1, DT, &dt);
MPI_Type_commit(&dt);
return dt;
}
static MPI_Datatype sliceLocation () {
constexpr int n = 2;
// create a sliceLocation to measure in the current architecture
// the packing of the struct
Slice<F>::Location measure;
MPI_Datatype dt;
const std::vector<int> lengths(n, 1);
const MPI_Datatype types[n] = {usizeDt(), usizeDt()};
static_assert(sizeof(Slice<F>::Location) == 2 * sizeof(size_t),
"The Location packing is wrong in your compiler");
// measure the displacements in the struct
size_t j = 0;
MPI_Aint base_address, displacements[n];
MPI_Get_address(&measure, &base_address);
MPI_Get_address(&measure.rank, &displacements[j++]);
MPI_Get_address(&measure.source, &displacements[j++]);
for (size_t i = 0; i < n; i++)
displacements[i] = MPI_Aint_diff(displacements[i], base_address);
MPI_Type_create_struct(n, lengths.data(), displacements, types, &dt);
MPI_Type_commit(&dt);
return dt;
}
static MPI_Datatype usizeDt() { return MPI_UINT64_T; }
static MPI_Datatype sliceInfo () {
constexpr int n = 5;
MPI_Datatype dt;
Slice<F>::Info measure;
const std::vector<int> lengths(n, 1);
const MPI_Datatype types[n]
= { vector(2, usizeDt())
, vector(sizeof(enum Type), MPI_CHAR)
, vector(sizeof(enum State), MPI_CHAR)
, sliceLocation()
, vector(sizeof(enum Type), MPI_CHAR)
// TODO: Why this does not work on intel mpi?
/*, MPI_UINT64_T*/
};
static_assert(sizeof(enum Type) == 4, "Enum type not 4 bytes long");
static_assert(sizeof(enum State) == 4, "Enum State not 4 bytes long");
static_assert(sizeof(enum Name) == 4, "Enum Name not 4 bytes long");
// create the displacements from the info measurement struct
size_t j = 0;
MPI_Aint base_address, displacements[n];
MPI_Get_address(&measure, &base_address);
MPI_Get_address(&measure.tuple[0], &displacements[j++]);
MPI_Get_address(&measure.type, &displacements[j++]);
MPI_Get_address(&measure.state, &displacements[j++]);
MPI_Get_address(&measure.from, &displacements[j++]);
MPI_Get_address(&measure.recycling, &displacements[j++]);
for (size_t i = 0; i < n; i++)
displacements[i] = MPI_Aint_diff(displacements[i], base_address);
MPI_Type_create_struct(n, lengths.data(), displacements, types, &dt);
MPI_Type_commit(&dt);
return dt;
}
static MPI_Datatype localDatabaseElement () {
constexpr int n = 2;
MPI_Datatype dt;
LocalDatabaseElement measure;
const std::vector<int> lengths(n, 1);
const MPI_Datatype types[n]
= { vector(sizeof(enum Name), MPI_CHAR)
, sliceInfo()
};
// measure the displacements in the struct
size_t j = 0;
MPI_Aint base_address, displacements[n];
MPI_Get_address(&measure, &base_address);
MPI_Get_address(&measure.name, &displacements[j++]);
MPI_Get_address(&measure.info, &displacements[j++]);
for (size_t i = 0; i < n; i++)
displacements[i] = MPI_Aint_diff(displacements[i], base_address);
static_assert( sizeof(LocalDatabaseElement) == sizeof(measure)
, "Measure has bad size");
MPI_Type_create_struct(n, lengths.data(), displacements, types, &dt);
MPI_Type_commit(&dt);
return vector(sizeof(LocalDatabaseElement), MPI_CHAR);
// TODO: write tests in order to know if this works
return dt;
}
};
2.10 Static utilities¶
This section presents some functions which are useful to work with slices and are inside the namespace created by the slice struct.
The function subtupleBySlice gives to every Slice::Type
its meaning in terms of the triples \((a,b,c)\).
Notice that since in general the relation
\(a < b < c\) holds (in our implementation), the case
of one-dimensional parametrizations A, B and C is well
defined.
The function should only throw if there is an implementation
error where the Slice::Type enum has been expanded and this
function has not been updated accordingly.
static
PartialTuple subtupleBySlice(ABCTuple abc, Type sliceType) {
switch (sliceType) {
case AB: return {abc[0], abc[1]};
case BC: return {abc[1], abc[2]};
case AC: return {abc[0], abc[2]};
case CB: return {abc[2], abc[1]};
case BA: return {abc[1], abc[0]};
case CA: return {abc[2], abc[0]};
case A: return {abc[0], 0};
case B: return {abc[1], 0};
case C: return {abc[2], 0};
default: throw "Switch statement not exhaustive!";
}
}
In the context of cleaning up slices during the main loop, it is important to check if a given slice has some slices referencing to it in quality of recycled slices.
This function should therefore return a vector of pointers of slices referencing to the given slice’s info, when the length of the vector is zero, then there are no dangling links.
static std::vector<Slice<F>*> hasRecycledReferencingToIt
( std::vector<Slice<F>> &slices
, Info const& info
) {
std::vector<Slice<F>*> result;
for (auto& s: slices)
if ( s.info.recycling == info.type
&& s.info.tuple == info.tuple
&& s.info.state == Recycled
) result.push_back(&s);
return result;
}
The rest of the coming functions are utilities in order to find in a vector
of slices a given slice by reference. Mostly they are merely convenience
wrappers to the standard library function std::find_if.
They are named as find<...>, where <...> represents some condition
and must always return a reference to the found slice, i.e., Slice&.
Atrip relies on these functions to find the sought for slices,
therefore these functions will throw a std::domain_error if the
given slice could not be found.
static Slice<F>& findOneByType(std::vector<Slice<F>> &slices, Slice<F>::Type type) {
const auto sliceIt
= std::find_if(slices.begin(), slices.end(),
[&type](Slice<F> const& s) {
return type == s.info.type;
});
WITH_CRAZY_DEBUG
WITH_RANK
<< "\t__ looking for " << type << "\n";
if (sliceIt == slices.end())
throw std::domain_error("Slice by type not found!");
return *sliceIt;
}
static Slice<F>&
findRecycledSource (std::vector<Slice<F>> &slices, Slice<F>::Info info) {
const auto sliceIt
= std::find_if(slices.begin(), slices.end(),
[&info](Slice<F> const& s) {
return info.recycling == s.info.type
&& info.tuple == s.info.tuple
&& State::Recycled != s.info.state
;
});
WITH_CRAZY_DEBUG
WITH_RANK << "__slice__:find: recycling source of "
<< pretty_print(info) << "\n";
if (sliceIt == slices.end())
throw std::domain_error( "Slice not found: "
+ pretty_print(info)
+ " rank: "
+ pretty_print(Atrip::rank)
);
WITH_RANK << "__slice__:find: " << pretty_print(sliceIt->info) << "\n";
return *sliceIt;
}
static Slice<F>& findByTypeAbc
( std::vector<Slice<F>> &slices
, Slice<F>::Type type
, ABCTuple const& abc
) {
const auto tuple = Slice<F>::subtupleBySlice(abc, type);
const auto sliceIt
= std::find_if(slices.begin(), slices.end(),
[&type, &tuple](Slice<F> const& s) {
return type == s.info.type
&& tuple == s.info.tuple
;
});
WITH_CRAZY_DEBUG
WITH_RANK << "__slice__:find:" << type << " and tuple "
<< pretty_print(tuple)
<< "\n";
if (sliceIt == slices.end())
throw std::domain_error( "Slice not found: "
+ pretty_print(tuple)
+ ", "
+ pretty_print(type)
+ " rank: "
+ pretty_print(Atrip::rank)
);
return *sliceIt;
}
static Slice<F>& findByInfo(std::vector<Slice<F>> &slices,
Slice<F>::Info const& info) {
const auto sliceIt
= std::find_if(slices.begin(), slices.end(),
[&info](Slice<F> const& s) {
// TODO: maybe implement comparison in Info struct
return info.type == s.info.type
&& info.state == s.info.state
&& info.tuple == s.info.tuple
&& info.from.rank == s.info.from.rank
&& info.from.source == s.info.from.source
;
});
WITH_CRAZY_DEBUG
WITH_RANK << "__slice__:find:looking for " << pretty_print(info) << "\n";
if (sliceIt == slices.end())
throw std::domain_error( "Slice by info not found: "
+ pretty_print(info));
return *sliceIt;
}
2.11 Attributes¶
A slice object does not own data, it is just a container or a pointer to data together with additional bookkeeping facilities.
It includes an info structure with the information about the slice,
Type, State etc, which will be later communicated to other ranks.
Info info;
A pointer to data is also necessary for the Slice but not necessary
to be communicated to other ranks. The Slice should never allocate
or deallocate itself the pointer.
DataPtr<F> data;
#if defined(HAVE_CUDA)
F* mpi_data;
#endif
An MPI_Request handle is also included so that the slices that are
to receive data through MPI can know which request they belong to.
MPI_Request request;
For practical purposes in MPI calls, the number of elements in data is also included.
const size_t size;
2.12 Member functions¶
It is important to note that a ready slice should not be recycled from any other slice, so that it can have access by itself to the data.
void markReady() noexcept {
info.state = Ready;
info.recycling = Blank;
}
The following function asks wether or not
the slice has effectively been unwrapped or not,
i.e., wether or not the data are accessible and already
there. This can only happen in two ways, either
is the slice Ready or it is SelfSufficient,
i.e., the data pointed to was pre-distributed to the current node.
bool isUnwrapped() const noexcept {
return info.state == Ready
|| info.state == SelfSufficient
;
}
The function isUnwrappable answers which slices can be unwrapped
potentially. Unwrapped slices can be unwrapped again idempotentially.
Also Recycled slices can be unwrapped, i.e. the slices pointed to by them
will be unwrapped.
The only other possibility is that the slice has been dispatched
in the past and can be unwrapped. The case where the state
is Dispatched is the canonical intuitive case where a real process
of unwrapping, i.e. waiting for the data to get through the network,
is done.
bool isUnwrappable() const noexcept {
return isUnwrapped()
|| info.state == Recycled
|| info.state == Dispatched
;
}
inline bool isDirectlyFetchable() const noexcept {
return info.state == Ready || info.state == Dispatched;
}
void free() noexcept {
info.tuple = {0, 0};
info.type = Blank;
info.state = Acceptor;
info.from = {0, 0};
info.recycling = Blank;
data = DataNullPtr;
}
inline bool isFree() const noexcept {
return info.tuple == PartialTuple{0, 0}
&& info.type == Blank
&& info.state == Acceptor
&& info.from.rank == 0
&& info.from.source == 0
&& info.recycling == Blank
&& data == DataNullPtr
;
}
The function isRecylable answers the question, which slices can be recycled.
A slice can only be recycled if it is Fetch or Ready and has a valid datapointer.
In particular, SelfSufficient are not recyclable, since it is easier just to create a SelfSufficient slice than deal with data dependencies.
Furthermore, a recycled slice is not recyclable, if this is the case then it is either bad design or a bug.
inline bool isRecyclable() const noexcept {
return ( info.state == Dispatched
|| info.state == Ready
|| info.state == Fetch
)
&& hasValidDataPointer()
;
}
The function hasValidDataPointer describes if a slice has a valid
data pointer.
This is important to know if the slice has some data to it, also
some structural checks are done, so that it should not be Acceptor
or Blank, if this is the case then it is a bug.
inline bool hasValidDataPointer() const noexcept {
return data != DataNullPtr
&& info.state != Acceptor
&& info.type != Blank
;
}
The function
unwrapAndMarkReady
calls the low-level MPI functions
in order to wait whenever the state of the slice is correct.
The main behaviour of the function should
return if state is
Ready, since then there is nothing to be done.throw if the state is not
Dispatched, only a dispatched slice can be unwrapped through MPI.throw if an MPI error happens.
void unwrapAndMarkReady() {
if (info.state == Ready) return;
if (info.state != Dispatched)
throw
std::domain_error("Can't unwrap a non-ready, non-dispatched slice!");
markReady();
MPI_Status status;
#ifdef HAVE_OCD
WITH_RANK << "__slice__:mpi: waiting " << "\n";
#endif
const int errorCode = MPI_Wait(&request, &status);
if (MPI_SUCCESS != MPI_Request_free(&request))
throw "Error freeing MPI request";
if (errorCode != MPI_SUCCESS)
throw "MPI ERROR HAPPENED....";
#if defined(HAVE_CUDA)
// copy the retrieved mpi data to the device
cuMemcpyHtoD(data, (void*)mpi_data, sizeof(F) * size);
std::free(mpi_data);
#endif
#ifdef HAVE_OCD
char errorString[MPI_MAX_ERROR_STRING];
int errorSize;
MPI_Error_string(errorCode, errorString, &errorSize);
WITH_RANK << "__slice__:mpi: status "
<< "{ .source=" << status.MPI_SOURCE
<< ", .tag=" << status.MPI_TAG
<< ", .error=" << status.MPI_ERROR
<< ", .errCode=" << errorCode
<< ", .err=" << errorString
<< " }"
<< "\n";
#endif
}
3 Utils¶
This section presents some utilities
3.1 Pretty printing¶
The pretty printing uses the dbg-macro package.
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-parameter"
template <typename T>
std::string pretty_print(T&& value) {
std::stringstream stream;
#if ATRIP_DEBUG > 2
dbg::pretty_print(stream, std::forward<T>(value));
#endif
return stream.str();
}
#pragma GCC diagnostic pop
3.2 Chrono¶
The chrono is just a simple wrapper for a high resolution clock
that can be found in the std::chrono namespace of the standard library.
#define WITH_CHRONO(__chrono_name, ...) \
Atrip::chrono[__chrono_name].start(); \
__VA_ARGS__ \
Atrip::chrono[__chrono_name].stop();
struct Timer {
using Clock = std::chrono::high_resolution_clock;
using Event = std::chrono::time_point<Clock>;
std::chrono::duration<double> duration;
Event _start;
inline void start() noexcept { _start = Clock::now(); }
inline void stop() noexcept { duration += Clock::now() - _start; }
inline void clear() noexcept { duration *= 0; }
inline double count() const noexcept { return duration.count(); }
};
using Timings = std::map<std::string, Timer>;
4 The rank mapping¶
This section introduces the concept of rank mapping, which defines how slices will be allocated to every rank.
#pragma once
#include <vector>
#include <algorithm>
#include <atrip/Slice.hpp>
#include <atrip/Tuples.hpp>
namespace atrip {
template <typename F=double>
struct RankMap {
static bool RANK_ROUND_ROBIN;
std::vector<size_t> const lengths;
size_t const np, size;
ClusterInfo const clusterInfo;
RankMap(std::vector<size_t> lens, size_t np_, MPI_Comm comm)
: lengths(lens)
, np(np_)
, size(std::accumulate(lengths.begin(), lengths.end(),
1UL, std::multiplies<size_t>()))
, clusterInfo(getClusterInfo(comm))
{ assert(lengths.size() <= 2); }
size_t find(typename Slice<F>::Location const& p) const noexcept {
if (RANK_ROUND_ROBIN) {
return p.source * np + p.rank;
} else {
const size_t
rankPosition = p.source * clusterInfo.ranksPerNode
+ clusterInfo.rankInfos[p.rank].localRank
;
return rankPosition * clusterInfo.nNodes
+ clusterInfo.rankInfos[p.rank].nodeId
;
}
}
size_t nSources() const noexcept {
return size / np + size_t(size % np != 0);
}
bool isPaddingRank(size_t rank) const noexcept {
return size % np == 0
? false
: rank > (size % np - 1)
;
}
bool isSourcePadding(const size_t rank, const size_t source)
const noexcept {
return source == nSources() && isPaddingRank(rank);
}
typename Slice<F>::Location
find(ABCTuple const& abc, typename Slice<F>::Type sliceType) const {
// tuple = {11, 8} when abc = {11, 8, 9} and sliceType = AB
// tuple = {11, 0} when abc = {11, 8, 9} and sliceType = A
const auto tuple = Slice<F>::subtupleBySlice(abc, sliceType);
const size_t index
= tuple[0]
+ tuple[1] * (lengths.size() > 1 ? lengths[0] : 0)
;
size_t rank, source;
if (RANK_ROUND_ROBIN) {
rank = index % np;
source = index / np;
} else {
size_t const
// the node that will be assigned to
nodeId = index % clusterInfo.nNodes
// how many times it has been assigned to the node
, s_n = index / clusterInfo.nNodes
// which local rank in the node should be
, localRank = s_n % clusterInfo.ranksPerNode
// and the local source (how many times we chose this local rank)
, localSource = s_n / clusterInfo.ranksPerNode
;
// find the localRank-th entry in clusterInfo
auto const& it =
std::find_if(clusterInfo.rankInfos.begin(),
clusterInfo.rankInfos.end(),
[nodeId, localRank](RankInfo const& ri) {
return ri.nodeId == nodeId
&& ri.localRank == localRank
;
});
if (it == clusterInfo.rankInfos.end()) {
throw "FATAL! Error in node distribution of the slices";
}
rank = (*it).globalRank;
source = localSource;
}
return
{ rank
, source
};
}
};
}
5 The slice union¶
6 Tuples¶
This section introduces the types for tuples \((a,b,c)\) as well as their distribution to nodes and cores.
6.1 Tuples types¶
The main tuple types are simple type aliases for finite-size arrays. A tuple is thus simply 3 natural numbers \((a,b,c)\) whereas a partial tuple is a two dimensional subset of these three.
using ABCTuple = std::array<size_t, 3>;
using PartialTuple = std::array<size_t, 2>;
using ABCTuples = std::vector<ABCTuple>;
constexpr ABCTuple FAKE_TUPLE = {0, 0, 0};
constexpr ABCTuple INVALID_TUPLE = {1, 1, 1};
6.2 Distributing the tuples¶
In general it is our task to distribute all the tuples
\((a,b,c)\) among the ranks. Every distribution should
make sure to allocate the same amount of tuples to every rank,
padding the list with FAKE_TUPLE elements as necessary.
The interface that we propose for this is simplye
struct TuplesDistribution {
virtual ABCTuples getTuples(size_t Nv, MPI_Comm universe) = 0;
virtual bool tupleIsFake(ABCTuple const& t) { return t == FAKE_TUPLE; }
};
6.3 Node information¶
- nodeList
List of hostnames of size \(N_n\)
- nodeInfos
List of (hostname, local rank Id) of size \(N_p\), i.e., size of ranks where local rank id goes from 0 to 48.
getNodeNames gets the names of the nodes used,
i.e., the size of the resulting vector gives the
number of nodes.
std::vector<std::string> getNodeNames(MPI_Comm comm){
int rank, np;
MPI_Comm_rank(comm, &rank);
MPI_Comm_size(comm, &np);
std::vector<std::string> nodeList(np);
char nodeName[MPI_MAX_PROCESSOR_NAME];
char *nodeNames = (char*)malloc(np * MPI_MAX_PROCESSOR_NAME);
std::vector<int> nameLengths(np)
, off(np)
;
int nameLength;
MPI_Get_processor_name(nodeName, &nameLength);
MPI_Allgather(&nameLength,
1,
MPI_INT,
nameLengths.data(),
1,
MPI_INT,
comm);
for (int i(1); i < np; i++)
off[i] = off[i-1] + nameLengths[i-1];
MPI_Allgatherv(nodeName,
nameLengths[rank],
MPI_BYTE,
nodeNames,
nameLengths.data(),
off.data(),
MPI_BYTE,
comm);
for (int i(0); i < np; i++) {
std::string const s(&nodeNames[off[i]], nameLengths[i]);
nodeList[i] = s;
}
std::free(nodeNames);
return nodeList;
}
getNodeInfos
struct RankInfo {
const std::string name;
const size_t nodeId;
const size_t globalRank;
const size_t localRank;
const size_t ranksPerNode;
};
template <typename A>
A unique(A const &xs) {
auto result = xs;
std::sort(std::begin(result), std::end(result));
auto const& last = std::unique(std::begin(result), std::end(result));
result.erase(last, std::end(result));
return result;
}
std::vector<RankInfo>
getNodeInfos(std::vector<string> const& nodeNames) {
std::vector<RankInfo> result;
auto const uniqueNames = unique(nodeNames);
auto const index = [&uniqueNames](std::string const& s) {
auto const& it = std::find(uniqueNames.begin(), uniqueNames.end(), s);
return std::distance(uniqueNames.begin(), it);
};
std::vector<size_t> localRanks(uniqueNames.size(), 0);
size_t globalRank = 0;
for (auto const& name: nodeNames) {
const size_t nodeId = index(name);
result.push_back({name,
nodeId,
globalRank++,
localRanks[nodeId]++,
(size_t)
std::count(nodeNames.begin(),
nodeNames.end(),
name)
});
}
return result;
}
struct ClusterInfo {
const size_t nNodes, np, ranksPerNode;
const std::vector<RankInfo> rankInfos;
};
ClusterInfo
getClusterInfo(MPI_Comm comm) {
auto const names = getNodeNames(comm);
auto const rankInfos = getNodeInfos(names);
return ClusterInfo {
unique(names).size(),
names.size(),
rankInfos[0].ranksPerNode,
rankInfos
};
}
6.4 Naive list¶
The naive implementation of the global tuples list is simple three for loops creating tuples of the sort \((a,b,c)\) where the following conditions are met at the same time:
\(a \leq b \leq c\)
\(a \neq b \land b \neq c\)
This means, \((1, 2, 3) , (1, 1, 3) , (1, 2, 2)\) are acceptable tuples wherease \((2, 1, 1)\) and \((1, 1, 1)\) are not.
ABCTuples getTuplesList(size_t Nv, size_t rank, size_t np) {
const size_t
// total number of tuples for the problem
n = Nv * (Nv + 1) * (Nv + 2) / 6 - Nv
// all ranks should have the same number of tuples_per_rank
, tuples_per_rank = n / np + size_t(n % np != 0)
// start index for the global tuples list
, start = tuples_per_rank * rank
// end index for the global tuples list
, end = tuples_per_rank * (rank + 1)
;
LOG(1,"Atrip") << "tuples_per_rank = " << tuples_per_rank << "\n";
WITH_RANK << "start, end = " << start << ", " << end << "\n";
ABCTuples result(tuples_per_rank, FAKE_TUPLE);
for (size_t a(0), r(0), g(0); a < Nv; a++)
for (size_t b(a); b < Nv; b++)
for (size_t c(b); c < Nv; c++){
if ( a == b && b == c ) continue;
if ( start <= g && g < end) result[r++] = {a, b, c};
g++;
}
return result;
}
and all tuples would simply be
ABCTuples getAllTuplesList(const size_t Nv) {
const size_t n = Nv * (Nv + 1) * (Nv + 2) / 6 - Nv;
ABCTuples result(n);
for (size_t a(0), u(0); a < Nv; a++)
for (size_t b(a); b < Nv; b++)
for (size_t c(b); c < Nv; c++){
if ( a == b && b == c ) continue;
result[u++] = {a, b, c};
}
return result;
}
With getTupleList we can easily define a tuple distribution like
struct NaiveDistribution : public TuplesDistribution {
ABCTuples getTuples(size_t Nv, MPI_Comm universe) override {
int rank, np;
MPI_Comm_rank(universe, &rank);
MPI_Comm_size(universe, &np);
return getTuplesList(Nv, (size_t)rank, (size_t)np);
}
};
6.5 Group and sort list¶
6.5.1 Utils¶
// Provides the node on which the slice-element is found
// Right now we distribute the slices in a round robin fashion
// over the different nodes (NOTE: not mpi ranks but nodes)
inline
size_t isOnNode(size_t tuple, size_t nNodes) { return tuple % nNodes; }
// return the node (or all nodes) where the elements of this
// tuple are located
std::vector<size_t> getTupleNodes(ABCTuple const& t, size_t nNodes) {
std::vector<size_t>
nTuple = { isOnNode(t[0], nNodes)
, isOnNode(t[1], nNodes)
, isOnNode(t[2], nNodes)
};
return unique(nTuple);
}
struct Info {
size_t nNodes;
size_t nodeId;
};
6.5.2 Distribution¶
wording: home element = element which is located on the given node
we distribute the tuples such that each tuple has at least one ‘home element’
we sort each tuple in a way that the ‘home element’ are the fastest indices
we sort the list of tuples on every node
we resort the tuples that for every tuple abc the following holds: a<b<c
ABCTuples specialDistribution(Info const& info, ABCTuples const& allTuples) {
ABCTuples nodeTuples;
size_t const nNodes(info.nNodes);
std::vector<ABCTuples>
container1d(nNodes)
, container2d(nNodes * nNodes)
, container3d(nNodes * nNodes * nNodes)
;
WITH_DBG if (info.nodeId == 0)
std::cout << "\tGoing through all "
<< allTuples.size()
<< " tuples in "
<< nNodes
<< " nodes\n";
// build container-n-d's
for (auto const& t: allTuples) {
// one which node(s) are the tuple elements located...
// put them into the right container
auto const _nodes = getTupleNodes(t, nNodes);
switch (_nodes.size()) {
case 1:
container1d[_nodes[0]].push_back(t);
break;
case 2:
container2d[ _nodes[0]
+ _nodes[1] * nNodes
].push_back(t);
break;
case 3:
container3d[ _nodes[0]
+ _nodes[1] * nNodes
+ _nodes[2] * nNodes * nNodes
].push_back(t);
break;
}
}
WITH_DBG if (info.nodeId == 0)
std::cout << "\tBuilding 1-d containers\n";
// DISTRIBUTE 1-d containers
// every tuple which is only located at one node belongs to this node
{
auto const& _tuples = container1d[info.nodeId];
nodeTuples.resize(_tuples.size(), INVALID_TUPLE);
std::copy(_tuples.begin(), _tuples.end(), nodeTuples.begin());
}
WITH_DBG if (info.nodeId == 0)
std::cout << "\tBuilding 2-d containers\n";
// DISTRIBUTE 2-d containers
//the tuples which are located at two nodes are half/half given to these nodes
for (size_t yx = 0; yx < container2d.size(); yx++) {
auto const& _tuples = container2d[yx];
const
size_t idx = yx % nNodes
// remeber: yx = idy * nNodes + idx
, idy = yx / nNodes
, n_half = _tuples.size() / 2
, size = nodeTuples.size()
;
size_t nbeg, nend;
if (info.nodeId == idx) {
nbeg = 0 * n_half;
nend = n_half;
} else if (info.nodeId == idy) {
nbeg = 1 * n_half;
nend = _tuples.size();
} else {
// either idx or idy is my node
continue;
}
size_t const nextra = nend - nbeg;
nodeTuples.resize(size + nextra, INVALID_TUPLE);
std::copy(_tuples.begin() + nbeg,
_tuples.begin() + nend,
nodeTuples.begin() + size);
}
WITH_DBG if (info.nodeId == 0)
std::cout << "\tBuilding 3-d containers\n";
// DISTRIBUTE 3-d containers
for (size_t zyx = 0; zyx < container3d.size(); zyx++) {
auto const& _tuples = container3d[zyx];
const
size_t idx = zyx % nNodes
, idy = (zyx / nNodes) % nNodes
// remember: zyx = idx + idy * nNodes + idz * nNodes^2
, idz = zyx / nNodes / nNodes
, n_third = _tuples.size() / 3
, size = nodeTuples.size()
;
size_t nbeg, nend;
if (info.nodeId == idx) {
nbeg = 0 * n_third;
nend = 1 * n_third;
} else if (info.nodeId == idy) {
nbeg = 1 * n_third;
nend = 2 * n_third;
} else if (info.nodeId == idz) {
nbeg = 2 * n_third;
nend = _tuples.size();
} else {
// either idx or idy or idz is my node
continue;
}
size_t const nextra = nend - nbeg;
nodeTuples.resize(size + nextra, INVALID_TUPLE);
std::copy(_tuples.begin() + nbeg,
_tuples.begin() + nend,
nodeTuples.begin() + size);
}
WITH_DBG if (info.nodeId == 0) std::cout << "\tswapping tuples...\n";
/*
* sort part of group-and-sort algorithm
* every tuple on a given node is sorted in a way that
* the 'home elements' are the fastest index.
* 1:yyy 2:yyn(x) 3:yny(x) 4:ynn(x) 5:nyy 6:nyn(x) 7:nny 8:nnn
*/
for (auto &nt: nodeTuples){
if ( isOnNode(nt[0], nNodes) == info.nodeId ){ // 1234
if ( isOnNode(nt[2], nNodes) != info.nodeId ){ // 24
size_t const x(nt[0]);
nt[0] = nt[2]; // switch first and last
nt[2] = x;
}
else if ( isOnNode(nt[1], nNodes) != info.nodeId){ // 3
size_t const x(nt[0]);
nt[0] = nt[1]; // switch first two
nt[1] = x;
}
} else {
if ( isOnNode(nt[1], nNodes) == info.nodeId // 56
&& isOnNode(nt[2], nNodes) != info.nodeId
) { // 6
size_t const x(nt[1]);
nt[1] = nt[2]; // switch last two
nt[2] = x;
}
}
}
WITH_DBG if (info.nodeId == 0) std::cout << "\tsorting list of tuples...\n";
//now we sort the list of tuples
std::sort(nodeTuples.begin(), nodeTuples.end());
WITH_DBG if (info.nodeId == 0) std::cout << "\trestoring tuples...\n";
// we bring the tuples abc back in the order a<b<c
for (auto &t: nodeTuples) std::sort(t.begin(), t.end());
#if ATRIP_DEBUG > 1
WITH_DBG if (info.nodeId == 0)
std::cout << "checking for validity of " << nodeTuples.size() << std::endl;
const bool anyInvalid
= std::any_of(nodeTuples.begin(),
nodeTuples.end(),
[](ABCTuple const& t) { return t == INVALID_TUPLE; });
if (anyInvalid) throw "Some tuple is invalid in group-and-sort algorithm";
#endif
WITH_DBG if (info.nodeId == 0) std::cout << "\treturning tuples...\n";
return nodeTuples;
}
6.5.3 Main¶
The main routine should return the list of tuples to be handled by the current rank.
Let \(N_p\) be the number of ranks or processes. Let \(N_n\) be the number of nodes or sockets.
Then we have the following
Global rank | 0 1 2 3 4 5 6 7 8
key | global rank
nodeId | 0 1 0 1 1 0 2 2 2
Local rank | 0 0 1 1 2 2 0 1 2
intra color | 0 1 0 1 1 0 2 2 2
std::vector<ABCTuple> main(MPI_Comm universe, size_t Nv) {
int rank, np;
MPI_Comm_rank(universe, &rank);
MPI_Comm_size(universe, &np);
std::vector<ABCTuple> result;
auto const nodeNames(getNodeNames(universe));
size_t const nNodes = unique(nodeNames).size();
auto const nodeInfos = getNodeInfos(nodeNames);
// We want to construct a communicator which only contains of one
// element per node
bool const computeDistribution
= nodeInfos[rank].localRank == 0;
std::vector<ABCTuple>
nodeTuples
= computeDistribution
? specialDistribution(Info{nNodes, nodeInfos[rank].nodeId},
getAllTuplesList(Nv))
: std::vector<ABCTuple>()
;
LOG(1,"Atrip") << "got nodeTuples\n";
// now we have to send the data from **one** rank on each node
// to all others ranks of this node
const
int color = nodeInfos[rank].nodeId
, key = nodeInfos[rank].localRank
;
MPI_Comm INTRA_COMM;
MPI_Comm_split(universe, color, key, &INTRA_COMM);
Every node has to distribute **at least**
nodeTuples.size() / nodeInfos[rank].ranksPerNode
tuples among the ranks.
We have to communicate this quantity among all nodes.
size_t const
tuplesPerRankLocal
= nodeTuples.size() / nodeInfos[rank].ranksPerNode
+ size_t(nodeTuples.size() % nodeInfos[rank].ranksPerNode != 0)
;
size_t tuplesPerRankGlobal;
MPI_Reduce(&tuplesPerRankLocal,
&tuplesPerRankGlobal,
1,
MPI_UINT64_T,
MPI_MAX,
0,
universe);
MPI_Bcast(&tuplesPerRankGlobal,
1,
MPI_UINT64_T,
0,
universe);
LOG(1,"Atrip") << "Tuples per rank: " << tuplesPerRankGlobal << "\n";
LOG(1,"Atrip") << "ranks per node " << nodeInfos[rank].ranksPerNode << "\n";
LOG(1,"Atrip") << "#nodes " << nNodes << "\n";
Now we have the tuples that every rank has to have, i.e.,
tuplesPerRankGlobal.
However before this,
the tuples in nodeTuples now have to be sent from the local rank
in every node to all the ranks in the given node,
and we have to make sure that every rank inside a given node
gets the same amount of tuples, in this case it should be
tuplesPerRankLocal, and in our node the total number
of tuples should be tuplesPerRankLocal * nodeInfos[rank].ranksPerNode,
however this might not be the case up to now due to divisibility issues.
Up to now we have exactly nodeTuples.size() tuples, we have to make sure by
resizing that the condition above is met, i.e., so we can resize
and add some fake tuples at the end as padding.
size_t const totalTuples
= tuplesPerRankGlobal * nodeInfos[rank].ranksPerNode;
if (computeDistribution) {
// pad with FAKE_TUPLEs
nodeTuples.insert(nodeTuples.end(),
totalTuples - nodeTuples.size(),
FAKE_TUPLE);
}
And now we can simply scatter the tuples in nodeTuples and send
tuplesPerRankGlobal to the different ranks in the node,
{
// construct mpi type for abctuple
MPI_Datatype MPI_ABCTUPLE;
MPI_Type_vector(nodeTuples[0].size(), 1, 1, MPI_UINT64_T, &MPI_ABCTUPLE);
MPI_Type_commit(&MPI_ABCTUPLE);
LOG(1,"Atrip") << "scattering tuples \n";
result.resize(tuplesPerRankGlobal);
MPI_Scatter(nodeTuples.data(),
tuplesPerRankGlobal,
MPI_ABCTUPLE,
result.data(),
tuplesPerRankGlobal,
MPI_ABCTUPLE,
0,
INTRA_COMM);
MPI_Type_free(&MPI_ABCTUPLE);
}
The next step is sending the tuples in the local root rank
to the other ranks in the node, this we do with the MPI function
MPI_Scatterv.
Every rank gets tuplesPerRankLocal tuples and
the nodeTuples vector is now homogeneous and divisible by the number
of ranks per node in our node.
Therefore, the displacements are simply the vector
\[\left\{
k * \mathrm{tuplesPerNodeLocal}
\mid
k \in
\left\{ 0
, \ldots
, \#\text{ranks in node} - 1
\right\}
\right\}\]
and the sendCounts vector is simply the constant vector
tuplesPerRankLocal of size ranksPerNode.
return result;
}
6.5.4 Interface¶
The distribution interface will then simply be
struct Distribution : public TuplesDistribution {
ABCTuples getTuples(size_t Nv, MPI_Comm universe) override {
return main(universe, Nv);
}
};
7 Unions¶
Every slice pertaining to every different tensor is sliced differently.
#pragma once
#include <atrip/SliceUnion.hpp>
namespace atrip {
template <typename F=double>
void sliceIntoVector
( std::vector<F> &v
, CTF::Tensor<F> &toSlice
, std::vector<int64_t> const low
, std::vector<int64_t> const up
, CTF::Tensor<F> const& origin
, std::vector<int64_t> const originLow
, std::vector<int64_t> const originUp
) {
// Thank you CTF for forcing me to do this
struct { std::vector<int> up, low; }
toSlice_ = { {up.begin(), up.end()}
, {low.begin(), low.end()} }
, origin_ = { {originUp.begin(), originUp.end()}
, {originLow.begin(), originLow.end()} }
;
WITH_OCD
WITH_RANK << "slicing into " << pretty_print(toSlice_.up)
<< "," << pretty_print(toSlice_.low)
<< " from " << pretty_print(origin_.up)
<< "," << pretty_print(origin_.low)
<< "\n";
#ifndef ATRIP_DONT_SLICE
toSlice.slice( toSlice_.low.data()
, toSlice_.up.data()
, 0.0
, origin
, origin_.low.data()
, origin_.up.data()
, 1.0);
memcpy(v.data(), toSlice.data, sizeof(F) * v.size());
#endif
}
template <typename F=double>
struct TAPHH : public SliceUnion<F> {
TAPHH( CTF::Tensor<F> const& sourceTensor
, size_t No
, size_t Nv
, size_t np
, MPI_Comm child_world
, MPI_Comm global_world
) : SliceUnion<F>( {Slice<F>::A, Slice<F>::B, Slice<F>::C}
, {Nv, No, No} // size of the slices
, {Nv}
, np
, child_world
, global_world
, Slice<F>::TA
, 6) {
this->init(sourceTensor);
}
void sliceIntoBuffer(size_t it, CTF::Tensor<F> &to, CTF::Tensor<F> const& from) override
{
const int Nv = this->sliceLength[0]
, No = this->sliceLength[1]
, a = this->rankMap.find({static_cast<size_t>(Atrip::rank), it});
;
sliceIntoVector<F>( this->sources[it]
, to, {0, 0, 0}, {Nv, No, No}
, from, {a, 0, 0, 0}, {a+1, Nv, No, No}
);
}
};
template <typename F=double>
struct HHHA : public SliceUnion<F> {
HHHA( CTF::Tensor<F> const& sourceTensor
, size_t No
, size_t Nv
, size_t np
, MPI_Comm child_world
, MPI_Comm global_world
) : SliceUnion<F>( {Slice<F>::A, Slice<F>::B, Slice<F>::C}
, {No, No, No} // size of the slices
, {Nv} // size of the parametrization
, np
, child_world
, global_world
, Slice<F>::VIJKA
, 6) {
this->init(sourceTensor);
}
void sliceIntoBuffer(size_t it, CTF::Tensor<F> &to, CTF::Tensor<F> const& from) override
{
const int No = this->sliceLength[0]
, a = this->rankMap.find({static_cast<size_t>(Atrip::rank), it})
;
sliceIntoVector<F>( this->sources[it]
, to, {0, 0, 0}, {No, No, No}
, from, {0, 0, 0, a}, {No, No, No, a+1}
);
}
};
template <typename F=double>
struct ABPH : public SliceUnion<F> {
ABPH( CTF::Tensor<F> const& sourceTensor
, size_t No
, size_t Nv
, size_t np
, MPI_Comm child_world
, MPI_Comm global_world
) : SliceUnion<F>( { Slice<F>::AB, Slice<F>::BC, Slice<F>::AC
, Slice<F>::BA, Slice<F>::CB, Slice<F>::CA
}
, {Nv, No} // size of the slices
, {Nv, Nv} // size of the parametrization
, np
, child_world
, global_world
, Slice<F>::VABCI
, 2*6) {
this->init(sourceTensor);
}
void sliceIntoBuffer(size_t it, CTF::Tensor<F> &to, CTF::Tensor<F> const& from) override {
const int Nv = this->sliceLength[0]
, No = this->sliceLength[1]
, el = this->rankMap.find({static_cast<size_t>(Atrip::rank), it})
, a = el % Nv
, b = el / Nv
;
sliceIntoVector<F>( this->sources[it]
, to, {0, 0}, {Nv, No}
, from, {a, b, 0, 0}, {a+1, b+1, Nv, No}
);
}
};
template <typename F=double>
struct ABHH : public SliceUnion<F> {
ABHH( CTF::Tensor<F> const& sourceTensor
, size_t No
, size_t Nv
, size_t np
, MPI_Comm child_world
, MPI_Comm global_world
) : SliceUnion<F>( {Slice<F>::AB, Slice<F>::BC, Slice<F>::AC}
, {No, No} // size of the slices
, {Nv, Nv} // size of the parametrization
, np
, child_world
, global_world
, Slice<F>::VABIJ
, 6) {
this->init(sourceTensor);
}
void sliceIntoBuffer(size_t it, CTF::Tensor<F> &to, CTF::Tensor<F> const& from) override {
const int Nv = from.lens[0]
, No = this->sliceLength[1]
, el = this->rankMap.find({static_cast<size_t>(Atrip::rank), it})
, a = el % Nv
, b = el / Nv
;
sliceIntoVector<F>( this->sources[it]
, to, {0, 0}, {No, No}
, from, {a, b, 0, 0}, {a+1, b+1, No, No}
);
}
};
template <typename F=double>
struct TABHH : public SliceUnion<F> {
TABHH( CTF::Tensor<F> const& sourceTensor
, size_t No
, size_t Nv
, size_t np
, MPI_Comm child_world
, MPI_Comm global_world
) : SliceUnion<F>( {Slice<F>::AB, Slice<F>::BC, Slice<F>::AC}
, {No, No} // size of the slices
, {Nv, Nv} // size of the parametrization
, np
, child_world
, global_world
, Slice<F>::TABIJ
, 6) {
this->init(sourceTensor);
}
void sliceIntoBuffer(size_t it, CTF::Tensor<F> &to, CTF::Tensor<F> const& from) override {
// TODO: maybe generalize this with ABHH
const int Nv = from.lens[0]
, No = this->sliceLength[1]
, el = this->rankMap.find({static_cast<size_t>(Atrip::rank), it})
, a = el % Nv
, b = el / Nv
;
sliceIntoVector<F>( this->sources[it]
, to, {0, 0}, {No, No}
, from, {a, b, 0, 0}, {a+1, b+1, No, No}
);
}
};
}
8 Equations¶
This section presents on of the main physical contents of the program, the equations used.
The equations are of two types, energy equations and intermediate tensor contractions.
8.1 Energy¶
For the energy we have two types of functions, a function for when the tuples \((a,b,c)\) are distinct and where they are the same, they are accordingly called:
template <typename F=double>
double getEnergyDistinct
( F const epsabc
, size_t const No
, F* const epsi
, F* const Tijk
, F* const Zijk
);
template <typename F=double>
double getEnergySame
( F const epsabc
, size_t const No
, F* const epsi
, F* const Tijk
, F* const Zijk
);
Their implementations follow, and they are
written in a cache-friendly style for CPU architectures
through the blockSize variable.
template <typename F=double>
double getEnergyDistinct
( F const epsabc
, size_t const No
, F* const epsi
, F* const Tijk
, F* const Zijk
) {
constexpr size_t blockSize=16;
F energy(0.);
for (size_t kk=0; kk<No; kk+=blockSize){
const size_t kend( std::min(No, kk+blockSize) );
for (size_t jj(kk); jj<No; jj+=blockSize){
const size_t jend( std::min( No, jj+blockSize) );
for (size_t ii(jj); ii<No; ii+=blockSize){
const size_t iend( std::min( No, ii+blockSize) );
for (size_t k(kk); k < kend; k++){
const F ek(epsi[k]);
const size_t jstart = jj > k ? jj : k;
for (size_t j(jstart); j < jend; j++){
F const ej(epsi[j]);
F const facjk = j == k ? F(0.5) : F(1.0);
size_t istart = ii > j ? ii : j;
for (size_t i(istart); i < iend; i++){
const F
ei(epsi[i])
, facij = i == j ? F(0.5) : F(1.0)
, denominator(epsabc - ei - ej - ek)
, U(Zijk[i + No*j + No*No*k])
, V(Zijk[i + No*k + No*No*j])
, W(Zijk[j + No*i + No*No*k])
, X(Zijk[j + No*k + No*No*i])
, Y(Zijk[k + No*i + No*No*j])
, Z(Zijk[k + No*j + No*No*i])
, A(maybeConjugate<F>(Tijk[i + No*j + No*No*k]))
, B(maybeConjugate<F>(Tijk[i + No*k + No*No*j]))
, C(maybeConjugate<F>(Tijk[j + No*i + No*No*k]))
, D(maybeConjugate<F>(Tijk[j + No*k + No*No*i]))
, E(maybeConjugate<F>(Tijk[k + No*i + No*No*j]))
, _F(maybeConjugate<F>(Tijk[k + No*j + No*No*i]))
, value
= 3.0 * ( A * U
+ B * V
+ C * W
+ D * X
+ E * Y
+ _F * Z )
+ ( ( U + X + Y )
- 2.0 * ( V + W + Z )
) * ( A + D + E )
+ ( ( V + W + Z )
- 2.0 * ( U + X + Y )
) * ( B + C + _F )
;
energy += 2.0 * value / denominator * facjk * facij;
} // i
} // j
} // k
} // ii
} // jj
} // kk
return std::real(energy);
}
template <typename F=double>
double getEnergySame
( F const epsabc
, size_t const No
, F* const epsi
, F* const Tijk
, F* const Zijk
) {
constexpr size_t blockSize = 16;
F energy = F(0.);
for (size_t kk=0; kk<No; kk+=blockSize){
const size_t kend( std::min( kk+blockSize, No) );
for (size_t jj(kk); jj<No; jj+=blockSize){
const size_t jend( std::min( jj+blockSize, No) );
for (size_t ii(jj); ii<No; ii+=blockSize){
const size_t iend( std::min( ii+blockSize, No) );
for (size_t k(kk); k < kend; k++){
const F ek(epsi[k]);
const size_t jstart = jj > k ? jj : k;
for(size_t j(jstart); j < jend; j++){
const F facjk( j == k ? F(0.5) : F(1.0));
const F ej(epsi[j]);
const size_t istart = ii > j ? ii : j;
for(size_t i(istart); i < iend; i++){
const F
ei(epsi[i])
, facij ( i==j ? F(0.5) : F(1.0))
, denominator(epsabc - ei - ej - ek)
, U(Zijk[i + No*j + No*No*k])
, V(Zijk[j + No*k + No*No*i])
, W(Zijk[k + No*i + No*No*j])
, A(maybeConjugate<F>(Tijk[i + No*j + No*No*k]))
, B(maybeConjugate<F>(Tijk[j + No*k + No*No*i]))
, C(maybeConjugate<F>(Tijk[k + No*i + No*No*j]))
, value
= F(3.0) * ( A * U
+ B * V
+ C * W
)
- ( A + B + C ) * ( U + V + W )
;
energy += F(2.0) * value / denominator * facjk * facij;
} // i
} // j
} // k
} // ii
} // jj
} // kk
return std::real(energy);
}
And we explicitly instantiate these templated functions:
// instantiate double
template
double getEnergyDistinct
( double const epsabc
, size_t const No
, double* const epsi
, double* const Tijk
, double* const Zijk
);
template
double getEnergySame
( double const epsabc
, size_t const No
, double* const epsi
, double* const Tijk
, double* const Zijk
);
// instantiate Complex
template
double getEnergyDistinct
( Complex const epsabc
, size_t const No
, Complex* const epsi
, Complex* const Tijk
, Complex* const Zijk
);
template
double getEnergySame
( Complex const epsabc
, size_t const No
, Complex* const epsi
, Complex* const Tijk
, Complex* const Zijk
);
8.2 Contractions¶
8.2.1 Singles contribution¶
The first contraction we discuss is the one involving \(t_i^a\) singles amplitudes.
For every \((a,b,c)\) we construct the object
begin{align*} Z_{ijk} &= t^a_i V^{bc}_{jk} \
& + t^b_j V^{ac}_{ik} \ & + t^c_k V^{ab}_{ij}
end{align*}
and for it we will need the corresponding slices
of the \(V^{pp}_{ph}\) integrals, i.e., AB, AC and BC.
template <typename F=double>
void singlesContribution
( size_t No
, size_t Nv
, const ABCTuple &abc
, F* const Tph
, F* const VABij
, F* const VACij
, F* const VBCij
, F* Zijk
) {
const size_t a(abc[0]), b(abc[1]), c(abc[2]);
const size_t NoNo = No*No;
// TODO: change order of for loops
for (size_t k = 0; k < No; k++)
for (size_t i = 0; i < No; i++)
for (size_t j = 0; j < No; j++) {
const size_t ijk = i + j*No + k*NoNo;
Zijk[ijk] += Tph[ a + i * Nv ] * VBCij[ j + k * No ];
Zijk[ijk] += Tph[ b + j * Nv ] * VACij[ i + k * No ];
Zijk[ijk] += Tph[ c + k * Nv ] * VABij[ i + j * No ];
}
}
// instantiate
template void singlesContribution( size_t No
, size_t Nv
, const ABCTuple &abc
, double* const Tph
, double* const VABij
, double* const VACij
, double* const VBCij
, double* Zijk
);
template void singlesContribution( size_t No
, size_t Nv
, const ABCTuple &abc
, Complex* const Tph
, Complex* const VABij
, Complex* const VACij
, Complex* const VBCij
, Complex* Zijk
);
8.2.2 Doubles contribution¶
Next we build the tensor \(T_{ijk}\) from the contraction of the coulomb integrals with the doubles amplitudes \(t^{ab}_{ij}\). There are two kinds of contractions, which are differently challenging in terms of computation.
begin{align*} T_{ijk} &= \ sum_{{color{red}L} = 0}^{N_mathrm{o}} &- T^{{color{blue} ab}}_{{color{red}L}j} V_{ik}^{{color{red}L}{color{blue}c}} \ &- T^{{color{blue} ab}}_{i{color{red}L}} V_{jk}^{{color{red}L}{color{blue}c}} \ &- T^{{color{blue} ac}}_{{color{red}L}k} V_{ij}^{{color{red}L}{color{blue}b}} \ &- T^{{color{blue} ac}}_{i{color{red}L}} V_{kj}^{{color{red}L}{color{blue}b}} \ &- T^{{color{blue} bc}}_{{color{red}L}k} V_{ji}^{{color{red}L}{color{blue}a}} \ &- T^{{color{blue} bc}}_{j{color{red}L}} V_{ki}^{{color{red}L}{color{blue}a}} end{align*}
begin{align*} T_{ijk} &= \ sum_{{color{red}e} = 0}^{N_mathrm{v}}
&phantom{+}
- V^{{color{blue}ab}}_{{color{red}e}i} T^{{color{blue}c}{color{red}e}}_{ij} \
&+ V^{{color{blue}ba}}_{{color{red}e}j} T^{{color{blue}c}{color{red}e}}_{ji} \ &+ V^{{color{blue}cb}}_{{color{red}e}k} T^{{color{blue}a}{color{red}e}}_{kj} \ &+ V^{{color{blue}ac}}_{{color{red}e}i} T^{{color{blue}b}{color{red}e}}_{ik} \ &+ V^{{color{blue}bc}}_{{color{red}e}j} T^{{color{blue}a}{color{red}e}}_{jk} \ &+ V^{{color{blue}ca}}_{{color{red}e}k} T^{{color{blue}b}{color{red}e}}_{ki} \
end{align*}
#if defined(HAVE_CUDA)
__device__
#endif
template <typename F=double>
void doublesContribution
( const ABCTuple &abc
, size_t const No
, size_t const Nv
// -- VABCI
, DataPtr<F> const VABph
, DataPtr<F> const VACph
, DataPtr<F> const VBCph
, DataPtr<F> const VBAph
, DataPtr<F> const VCAph
, DataPtr<F> const VCBph
// -- VHHHA
, DataPtr<F> const VhhhA
, DataPtr<F> const VhhhB
, DataPtr<F> const VhhhC
// -- TA
, DataPtr<F> const TAphh
, DataPtr<F> const TBphh
, DataPtr<F> const TCphh
// -- TABIJ
, DataPtr<F> const TABhh
, DataPtr<F> const TAChh
, DataPtr<F> const TBChh
// -- TIJK
// , DataPtr<F> Tijk
, DataFieldType<F>* Tijk_
);
#if defined(HAVE_CUDA)
__device__
#endif
template <typename F=double>
void doublesContribution
( const ABCTuple &abc
, size_t const No
, size_t const Nv
// -- VABCI
, DataPtr<F> const VABph
, DataPtr<F> const VACph
, DataPtr<F> const VBCph
, DataPtr<F> const VBAph
, DataPtr<F> const VCAph
, DataPtr<F> const VCBph
// -- VHHHA
, DataPtr<F> const VhhhA
, DataPtr<F> const VhhhB
, DataPtr<F> const VhhhC
// -- TA
, DataPtr<F> const TAphh
, DataPtr<F> const TBphh
, DataPtr<F> const TCphh
// -- TABIJ
, DataPtr<F> const TABhh
, DataPtr<F> const TAChh
, DataPtr<F> const TBChh
// -- TIJK
// , DataPtr<F> Tijk_
, DataFieldType<F>* Tijk_
) {
const size_t a = abc[0], b = abc[1], c = abc[2]
, NoNo = No*No, NoNv = No*Nv
;
typename DataField<F>::type* Tijk = (typename DataField<F>::type*) Tijk_;
#if defined(ATRIP_USE_DGEMM)
#define _IJK_(i, j, k) i + j*No + k*NoNo
#define REORDER(__II, __JJ, __KK) \
WITH_CHRONO("doubles:reorder", \
for (size_t k = 0; k < No; k++) \
for (size_t j = 0; j < No; j++) \
for (size_t i = 0; i < No; i++) { \
Tijk[_IJK_(i, j, k)] += _t_buffer_p[_IJK_(__II, __JJ, __KK)]; \
} \
)
#if defined(HAVE_CUDA)
#define __TO_DEVICEPTR(_v) \
((DataFieldType<F>*) \
(CUdeviceptr) \
thrust::raw_pointer_cast((_v)))
#define DGEMM_PARTICLES(__A, __B) \
atrip::xgemm<F>("T", \
"N", \
(int const*)&NoNo, \
(int const*)&No, \
(int const*)&Nv, \
&one, \
(DataFieldType<F>*)__A, \
(int const*)&Nv, \
(DataFieldType<F>*)__B, \
(int const*)&Nv, \
&zero, \
_t_buffer_p, \
(int const*)&NoNo);
#define DGEMM_HOLES(__A, __B, __TRANSB) \
atrip::xgemm<F>("N", \
__TRANSB, \
(int const*)&NoNo, \
(int const*)&No, \
(int const*)&No, \
&m_one, \
__TO_DEVICEPTR(__A), \
(int const*)&NoNo, \
(DataFieldType<F>*)__B, \
(int const*)&No, \
&zero, \
_t_buffer_p, \
(int const*)&NoNo \
);
#define MAYBE_CONJ(_conj, _buffer) \
for (size_t __i = 0; __i < NoNoNo; ++__i) \
__TO_DEVICEPTR(_conj.data())[__i] = \
maybeConjugate<DataFieldType<F>>(((DataFieldType<F>*)(_buffer))[__i]);
#else
#define __TO_DEVICEPTR(_v) (_v)
#define DGEMM_PARTICLES(__A, __B) \
atrip::xgemm<F>("T", \
"N", \
(int const*)&NoNo, \
(int const*)&No, \
(int const*)&Nv, \
&one, \
__A, \
(int const*)&Nv, \
__B, \
(int const*)&Nv, \
&zero, \
_t_buffer_p, \
(int const*)&NoNo \
);
#define DGEMM_HOLES(__A, __B, __TRANSB) \
atrip::xgemm<F>("N", \
__TRANSB, \
(int const*)&NoNo, \
(int const*)&No, \
(int const*)&No, \
&m_one, \
__A, \
(int const*)&NoNo, \
__B, \
(int const*)&No, \
&zero, \
_t_buffer_p, \
(int const*)&NoNo \
);
#define MAYBE_CONJ(_conj, _buffer) \
for (size_t __i = 0; __i < NoNoNo; ++__i) \
_conj[__i] = maybeConjugate<F>(_buffer[__i]);
#endif
const size_t NoNoNo = No*NoNo;
#ifdef HAVE_CUDA
thrust::device_vector< DataFieldType<F> > _t_buffer;
#else
std::vector<F> _t_buffer;
#endif
_t_buffer.reserve(NoNoNo);
DataFieldType<F>* _t_buffer_p = __TO_DEVICEPTR(_t_buffer.data());
F one{1.0}, m_one{-1.0}, zero{0.0};
WITH_CHRONO("double:reorder",
for (size_t k = 0; k < NoNoNo; k++) {
Tijk[k] = DataFieldType<F>{0.0};
})
// TOMERGE: replace chronos
WITH_CHRONO("doubles:holes",
{ // Holes part %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#ifdef HAVE_CUDA
thrust::device_vector< DataFieldType<F> > _vhhh;
_vhhh.reserve(NoNoNo);
#else
std::vector<F> _vhhh(NoNoNo);
#endif
// VhhhC[i + k*No + L*NoNo] * TABhh[L + j*No]; H1
MAYBE_CONJ(_vhhh, VhhhC)
WITH_CHRONO("doubles:holes:1",
DGEMM_HOLES(_vhhh.data(), TABhh, "N")
REORDER(i, k, j)
)
// VhhhC[j + k*No + L*NoNo] * TABhh[i + L*No]; H0
WITH_CHRONO("doubles:holes:2",
DGEMM_HOLES(_vhhh.data(), TABhh, "T")
REORDER(j, k, i)
)
// VhhhB[i + j*No + L*NoNo] * TAChh[L + k*No]; H5
MAYBE_CONJ(_vhhh, VhhhB)
WITH_CHRONO("doubles:holes:3",
DGEMM_HOLES(_vhhh.data(), TAChh, "N")
REORDER(i, j, k)
)
// VhhhB[k + j*No + L*NoNo] * TAChh[i + L*No]; H3
WITH_CHRONO("doubles:holes:4",
DGEMM_HOLES(_vhhh.data(), TAChh, "T")
REORDER(k, j, i)
)
// VhhhA[j + i*No + L*NoNo] * TBChh[L + k*No]; H1
MAYBE_CONJ(_vhhh, VhhhA)
WITH_CHRONO("doubles:holes:5",
DGEMM_HOLES(_vhhh.data(), TBChh, "N")
REORDER(j, i, k)
)
// VhhhA[k + i*No + L*NoNo] * TBChh[j + L*No]; H4
WITH_CHRONO("doubles:holes:6",
DGEMM_HOLES(_vhhh.data(), TBChh, "T")
REORDER(k, i, j)
)
}
)
#undef MAYBE_CONJ
WITH_CHRONO("doubles:particles",
{ // Particle part %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
// TAphh[E + i*Nv + j*NoNv] * VBCph[E + k*Nv]; P0
WITH_CHRONO("doubles:particles:1",
DGEMM_PARTICLES(TAphh, VBCph)
REORDER(i, j, k)
)
// TAphh[E + i*Nv + k*NoNv] * VCBph[E + j*Nv]; P3
WITH_CHRONO("doubles:particles:2",
DGEMM_PARTICLES(TAphh, VCBph)
REORDER(i, k, j)
)
// TCphh[E + k*Nv + i*NoNv] * VABph[E + j*Nv]; P5
WITH_CHRONO("doubles:particles:3",
DGEMM_PARTICLES(TCphh, VABph)
REORDER(k, i, j)
)
// TCphh[E + k*Nv + j*NoNv] * VBAph[E + i*Nv]; P2
WITH_CHRONO("doubles:particles:4",
DGEMM_PARTICLES(TCphh, VBAph)
REORDER(k, j, i)
)
// TBphh[E + j*Nv + i*NoNv] * VACph[E + k*Nv]; P1
WITH_CHRONO("doubles:particles:5",
DGEMM_PARTICLES(TBphh, VACph)
REORDER(j, i, k)
)
// TBphh[E + j*Nv + k*NoNv] * VCAph[E + i*Nv]; P4
WITH_CHRONO("doubles:particles:6",
DGEMM_PARTICLES(TBphh, VCAph)
REORDER(j, k, i)
)
}
)
#undef REORDER
#undef DGEMM_HOLES
#undef DGEMM_PARTICLES
#undef _IJK_
#else
for (size_t k = 0; k < No; k++)
for (size_t j = 0; j < No; j++)
for (size_t i = 0; i < No; i++){
const size_t ijk = i + j*No + k*NoNo
, jk = j + k*No
;
Tijk[ijk] = 0.0; // :important
// HOLE DIAGRAMS: TABHH and VHHHA
for (size_t L = 0; L < No; L++){
// t[abLj] * V[Lcik] H1
// t[baLi] * V[Lcjk] H0 TODO: conjugate T for complex
Tijk[ijk] -= TABhh[L + j*No] * VhhhC[i + k*No + L*NoNo];
Tijk[ijk] -= TABhh[i + L*No] * VhhhC[j + k*No + L*NoNo];
// t[acLk] * V[Lbij] H5
// t[caLi] * V[Lbkj] H3
Tijk[ijk] -= TAChh[L + k*No] * VhhhB[i + j*No + L*NoNo];
Tijk[ijk] -= TAChh[i + L*No] * VhhhB[k + j*No + L*NoNo];
// t[bcLk] * V[Laji] H2
// t[cbLj] * V[Laki] H4
Tijk[ijk] -= TBChh[L + k*No] * VhhhA[j + i*No + L*NoNo];
Tijk[ijk] -= TBChh[j + L*No] * VhhhA[k + i*No + L*NoNo];
}
// PARTILCE DIAGRAMS: TAPHH and VABPH
for (size_t E = 0; E < Nv; E++) {
// t[aEij] * V[bcEk] P0
// t[aEik] * V[cbEj] P3 // TODO: CHECK THIS ONE, I DONT KNOW
Tijk[ijk] += TAphh[E + i*Nv + j*NoNv] * VBCph[E + k*Nv];
Tijk[ijk] += TAphh[E + i*Nv + k*NoNv] * VCBph[E + j*Nv];
// t[cEki] * V[abEj] P5
// t[cEkj] * V[baEi] P2
Tijk[ijk] += TCphh[E + k*Nv + i*NoNv] * VABph[E + j*Nv];
Tijk[ijk] += TCphh[E + k*Nv + j*NoNv] * VBAph[E + i*Nv];
// t[bEji] * V[acEk] P1
// t[bEjk] * V[caEi] P4
Tijk[ijk] += TBphh[E + j*Nv + i*NoNv] * VACph[E + k*Nv];
Tijk[ijk] += TBphh[E + j*Nv + k*NoNv] * VCAph[E + i*Nv];
}
}
#endif
}
// instantiate templates
#if defined(HAVE_CUDA)
__device__
#endif
template
void doublesContribution<double>
( const ABCTuple &abc
, size_t const No
, size_t const Nv
// -- VABCI
, DataPtr<double> const VABph
, DataPtr<double> const VACph
, DataPtr<double> const VBCph
, DataPtr<double> const VBAph
, DataPtr<double> const VCAph
, DataPtr<double> const VCBph
// -- VHHHA
, DataPtr<double> const VhhhA
, DataPtr<double> const VhhhB
, DataPtr<double> const VhhhC
// -- TA
, DataPtr<double> const TAphh
, DataPtr<double> const TBphh
, DataPtr<double> const TCphh
// -- TABIJ
, DataPtr<double> const TABhh
, DataPtr<double> const TAChh
, DataPtr<double> const TBChh
// -- TIJK
, DataFieldType<double>* Tijk
);
#if defined(HAVE_CUDA)
__device__
#endif
template
void doublesContribution<Complex>
( const ABCTuple &abc
, size_t const No
, size_t const Nv
// -- VABCI
, DataPtr<Complex> const VABph
, DataPtr<Complex> const VACph
, DataPtr<Complex> const VBCph
, DataPtr<Complex> const VBAph
, DataPtr<Complex> const VCAph
, DataPtr<Complex> const VCBph
// -- VHHHA
, DataPtr<Complex> const VhhhA
, DataPtr<Complex> const VhhhB
, DataPtr<Complex> const VhhhC
// -- TA
, DataPtr<Complex> const TAphh
, DataPtr<Complex> const TBphh
, DataPtr<Complex> const TCphh
// -- TABIJ
, DataPtr<Complex> const TABhh
, DataPtr<Complex> const TAChh
, DataPtr<Complex> const TBChh
// -- TIJK
, DataFieldType<Complex>* Tijk
);
9 Blas¶
The main matrix-matrix multiplication method used in this algorithm
is mainly using the DGEMM function, which we declare as
extern since it should be known only at link-time.
#include <atrip/Blas.hpp>
#include <atrip/Atrip.hpp>
#if defined(HAVE_CUDA)
# include <cstring>
static inline
cublasOperation_t char_to_cublasOperation(const char* trans) {
if (strncmp("N", trans, 1) == 0)
return CUBLAS_OP_N;
else if (strncmp("T", trans, 1) == 0)
return CUBLAS_OP_T;
else
return CUBLAS_OP_C;
}
#endif
namespace atrip {
template <>
void xgemm<double>(const char *transa,
const char *transb,
const int *m,
const int *n,
const int *k,
double *alpha,
const typename DataField<double>::type *A,
const int *lda,
const typename DataField<double>::type *B,
const int *ldb,
double *beta,
typename DataField<double>::type *C,
const int *ldc) {
#if defined(HAVE_CUDA)
cublasDgemm(Atrip::cuda.handle,
char_to_cublasOperation(transa),
char_to_cublasOperation(transb),
*m, *n, *k,
alpha, A, *lda,
B, *ldb, beta,
C, *ldc);
#else
dgemm_(transa, transb,
m, n, k,
alpha, A, lda,
B, ldb, beta,
C, ldc);
#endif
}
template <>
void xgemm<Complex>(const char *transa,
const char *transb,
const int *m,
const int *n,
const int *k,
Complex *alpha,
const typename DataField<Complex>::type *A,
const int *lda,
const typename DataField<Complex>::type *B,
const int *ldb,
Complex *beta,
typename DataField<Complex>::type *C,
const int *ldc) {
#if defined(HAVE_CUDA)
#pragma warning HAVE_CUDA
cuDoubleComplex
cu_alpha = {std::real(*alpha), std::imag(*alpha)},
cu_beta = {std::real(*beta), std::imag(*beta)};
cublasZgemm(Atrip::cuda.handle,
char_to_cublasOperation(transa),
char_to_cublasOperation(transb),
*m, *n, *k,
&cu_alpha,
A, *lda,
B, *ldb,
&cu_beta,
C, *ldc);
#else
zgemm_(transa, transb,
m, n, k,
alpha, A, lda,
B, ldb, beta,
C, ldc);
#endif
}
}
10 Atrip¶
10.1 Header¶
#pragma once
#include <sstream>
#include <string>
#include <map>
#include <mpi.h>
#include "config.h"
#if defined(HAVE_CUDA)
#include <cuda.h>
#define CUBLASAPI
#include <cublas_api.h>
#include <cublas_v2.h>
#endif
#include <atrip/Utils.hpp>
#include <atrip/Types.hpp>
#define ADD_ATTRIBUTE(_type, _name, _default) \
_type _name = _default; \
Input& with_ ## _name(_type i) { \
_name = i; \
return *this; \
}
namespace atrip {
struct Atrip {
static size_t rank;
static size_t np;
static MPI_Comm communicator;
static Timings chrono;
#if defined(HAVE_CUDA)
struct CudaContext {
cublasStatus_t status;
cublasHandle_t handle;
};
static CudaContext cuda;
#endif
static void init(MPI_Comm);
template <typename F=double>
struct Input {
CTF::Tensor<F> *ei = nullptr
, *ea = nullptr
, *Tph = nullptr
, *Tpphh = nullptr
, *Vpphh = nullptr
, *Vhhhp = nullptr
, *Vppph = nullptr
;
Input& with_epsilon_i(CTF::Tensor<F> * t) { ei = t; return *this; }
Input& with_epsilon_a(CTF::Tensor<F> * t) { ea = t; return *this; }
Input& with_Tai(CTF::Tensor<F> * t) { Tph = t; return *this; }
Input& with_Tabij(CTF::Tensor<F> * t) { Tpphh = t; return *this; }
Input& with_Vabij(CTF::Tensor<F> * t) { Vpphh = t; return *this; }
Input& with_Vijka(CTF::Tensor<F> * t) { Vhhhp = t; return *this; }
Input& with_Vabci(CTF::Tensor<F> * t) { Vppph = t; return *this; }
enum TuplesDistribution {
NAIVE,
GROUP_AND_SORT,
};
ADD_ATTRIBUTE(bool, deleteVppph, false)
ADD_ATTRIBUTE(bool, rankRoundRobin, false)
ADD_ATTRIBUTE(bool, chrono, false)
ADD_ATTRIBUTE(bool, barrier, false)
ADD_ATTRIBUTE(int, maxIterations, 0)
ADD_ATTRIBUTE(int, iterationMod, -1)
ADD_ATTRIBUTE(int, percentageMod, -1)
ADD_ATTRIBUTE(TuplesDistribution, tuplesDistribution, NAIVE)
ADD_ATTRIBUTE(std::string, checkpointPath, "atrip-checkpoint.yaml")
ADD_ATTRIBUTE(bool, readCheckpointIfExists, true)
ADD_ATTRIBUTE(bool, writeCheckpoint, true)
ADD_ATTRIBUTE(float, checkpointAtPercentage, 10)
ADD_ATTRIBUTE(size_t, checkpointAtEveryIteration, 0)
};
struct Output {
double energy;
};
template <typename F=double>
static Output run(Input<F> const& in);
};
}
#undef ADD_ATTRIBUTE
10.2 Main¶
#include <iomanip>
#include <atrip/Atrip.hpp>
#include <atrip/Utils.hpp>
#include <atrip/Equations.hpp>
#include <atrip/SliceUnion.hpp>
#include <atrip/Unions.hpp>
#include <atrip/Checkpoint.hpp>
using namespace atrip;
template <typename F> bool RankMap<F>::RANK_ROUND_ROBIN;
template bool RankMap<double>::RANK_ROUND_ROBIN;
template bool RankMap<Complex>::RANK_ROUND_ROBIN;
size_t Atrip::rank;
size_t Atrip::np;
#if defined(HAVE_CUDA)
typename Atrip::CudaContext Atrip::cuda;
#endif
MPI_Comm Atrip::communicator;
Timings Atrip::chrono;
// user printing block
IterationDescriptor IterationDescription::descriptor;
void atrip::registerIterationDescriptor(IterationDescriptor d) {
IterationDescription::descriptor = d;
}
void Atrip::init(MPI_Comm world) {
Atrip::communicator = world;
MPI_Comm_rank(world, (int*)&Atrip::rank);
MPI_Comm_size(world, (int*)&Atrip::np);
#if defined(HAVE_CUDA)
Atrip::cuda.status = cublasCreate(&Atrip::cuda.handle);
#endif
}
template <typename F>
Atrip::Output Atrip::run(Atrip::Input<F> const& in) {
const size_t np = Atrip::np;
const size_t rank = Atrip::rank;
MPI_Comm universe = Atrip::communicator;
const size_t No = in.ei->lens[0];
const size_t Nv = in.ea->lens[0];
LOG(0,"Atrip") << "No: " << No << "\n";
LOG(0,"Atrip") << "Nv: " << Nv << "\n";
LOG(0,"Atrip") << "np: " << np << "\n";
// allocate the three scratches, see piecuch
// we need local copies of the following tensors on every
// rank
std::vector<F> _epsi(No)
, _epsa(Nv)
, _Tai(No * Nv)
;
in.ei->read_all(_epsi.data());
in.ea->read_all(_epsa.data());
in.Tph->read_all(_Tai.data());
#if defined(HAVE_CUDA)
DataPtr<F> Tai, epsi, epsa;
cuMemAlloc(&Tai, sizeof(F) * _Tai.size());
cuMemAlloc(&epsi, sizeof(F) * _epsi.size());
cuMemAlloc(&epsa, sizeof(F) * _epsa.size());
cuMemcpyHtoD(Tai, (void*)_Tai.data(), sizeof(F) * _Tai.size());
cuMemcpyHtoD(epsi,(void*)_epsi.data(), sizeof(F) * _epsi.size());
cuMemcpyHtoD(epsa, (void*)_epsa.data(), sizeof(F) * _epsa.size());
DataPtr<F> Tijk, Zijk;
cuMemAlloc(&Tijk, sizeof(F) * No * No * No);
cuMemAlloc(&Zijk, sizeof(F) * No * No * No);
#else
std::vector<F> &Tai = _Tai, &epsi = _epsi, &epsa = _epsa;
std::vector<F> Tijk(No*No*No), Zijk(No*No*No);
#endif
RankMap<F>::RANK_ROUND_ROBIN = in.rankRoundRobin;
if (RankMap<F>::RANK_ROUND_ROBIN) {
LOG(0,"Atrip") << "Doing rank round robin slices distribution\n";
} else {
LOG(0,"Atrip")
<< "Doing node > local rank round robin slices distribution\n";
}
// COMMUNICATOR CONSTRUCTION ========================================={{{1
//
// Construct a new communicator living only on a single rank
int child_size = 1
, child_rank
;
const
int color = rank / child_size
, crank = rank % child_size
;
MPI_Comm child_comm;
if (np == 1) {
child_comm = universe;
} else {
MPI_Comm_split(universe, color, crank, &child_comm);
MPI_Comm_rank(child_comm, &child_rank);
MPI_Comm_size(child_comm, &child_size);
}
// BUILD SLICES PARAMETRIZED BY NV x NV =============================={{{1
WITH_CHRONO("nv-nv-slices",
LOG(0,"Atrip") << "BUILD NV x NV-SLICES\n";
ABPH<F> abph(*in.Vppph, (size_t)No, (size_t)Nv, (size_t)np, child_comm, universe);
ABHH<F> abhh(*in.Vpphh, (size_t)No, (size_t)Nv, (size_t)np, child_comm, universe);
TABHH<F> tabhh(*in.Tpphh, (size_t)No, (size_t)Nv, (size_t)np, child_comm, universe);
)
// delete the Vppph so that we don't have a HWM situation for the NV slices
if (in.deleteVppph) {
delete in.Vppph;
}
// BUILD SLICES PARAMETRIZED BY NV ==================================={{{1
WITH_CHRONO("nv-slices",
LOG(0,"Atrip") << "BUILD NV-SLICES\n";
TAPHH<F> taphh(*in.Tpphh, (size_t)No, (size_t)Nv, (size_t)np, child_comm, universe);
HHHA<F> hhha(*in.Vhhhp, (size_t)No, (size_t)Nv, (size_t)np, child_comm, universe);
)
// all tensors
std::vector< SliceUnion<F>* > unions = {&taphh, &hhha, &abph, &abhh, &tabhh};
// get tuples for the current rank
TuplesDistribution *distribution;
if (in.tuplesDistribution == Atrip::Input<F>::TuplesDistribution::NAIVE) {
LOG(0,"Atrip") << "Using the naive distribution\n";
distribution = new NaiveDistribution();
} else {
LOG(0,"Atrip") << "Using the group-and-sort distribution\n";
distribution = new group_and_sort::Distribution();
}
LOG(0,"Atrip") << "BUILDING TUPLE LIST\n";
WITH_CHRONO("tuples:build",
auto const tuplesList = distribution->getTuples(Nv, universe);
)
const size_t nIterations = tuplesList.size();
{
LOG(0,"Atrip") << "#iterations: "
<< nIterations
<< "/"
<< nIterations * np
<< "\n";
}
const size_t
iterationMod = (in.percentageMod > 0)
? nIterations * in.percentageMod / 100.0
: in.iterationMod
, iteration1Percent = nIterations * 0.01
;
auto const isFakeTuple
= [&tuplesList, distribution](size_t const i) {
return distribution->tupleIsFake(tuplesList[i]);
};
using Database = typename Slice<F>::Database;
auto communicateDatabase
= [ &unions
, np
] (ABCTuple const& abc, MPI_Comm const& c) -> Database {
WITH_CHRONO("db:comm:type:do",
auto MPI_LDB_ELEMENT = Slice<F>::mpi::localDatabaseElement();
)
WITH_CHRONO("db:comm:ldb",
typename Slice<F>::LocalDatabase ldb;
for (auto const& tensor: unions) {
auto const& tensorDb = tensor->buildLocalDatabase(abc);
ldb.insert(ldb.end(), tensorDb.begin(), tensorDb.end());
}
)
Database db(np * ldb.size(), ldb[0]);
WITH_CHRONO("oneshot-db:comm:allgather",
WITH_CHRONO("db:comm:allgather",
MPI_Allgather( ldb.data()
, ldb.size()
, MPI_LDB_ELEMENT
, db.data()
, ldb.size()
, MPI_LDB_ELEMENT
, c);
))
WITH_CHRONO("db:comm:type:free",
MPI_Type_free(&MPI_LDB_ELEMENT);
)
return db;
};
auto doIOPhase
= [&unions, &rank, &np, &universe] (Database const& db) {
const size_t localDBLength = db.size() / np;
size_t sendTag = 0
, recvTag = rank * localDBLength
;
// RECIEVE PHASE ======================================================
{
// At this point, we have already send to everyone that fits
auto const& begin = &db[rank * localDBLength]
, end = begin + localDBLength
;
for (auto it = begin; it != end; ++it) {
recvTag++;
auto const& el = *it;
auto& u = unionByName(unions, el.name);
WITH_DBG std::cout
<< rank << ":r"
<< "♯" << recvTag << " =>"
<< " «n" << el.name
<< ", t" << el.info.type
<< ", s" << el.info.state
<< "»"
<< " ⊙ {" << rank << "⇐" << el.info.from.rank
<< ", "
<< el.info.from.source << "}"
<< " ∴ {" << el.info.tuple[0]
<< ", "
<< el.info.tuple[1]
<< "}"
<< "\n"
;
WITH_CHRONO("db:io:recv",
u.receive(el.info, recvTag);
)
} // recv
}
// SEND PHASE =========================================================
for (size_t otherRank = 0; otherRank<np; otherRank++) {
auto const& begin = &db[otherRank * localDBLength]
, end = begin + localDBLength
;
for (auto it = begin; it != end; ++it) {
sendTag++;
typename Slice<F>::LocalDatabaseElement const& el = *it;
if (el.info.from.rank != rank) continue;
auto& u = unionByName(unions, el.name);
WITH_DBG std::cout
<< rank << ":s"
<< "♯" << sendTag << " =>"
<< " «n" << el.name
<< ", t" << el.info.type
<< ", s" << el.info.state
<< "»"
<< " ⊙ {" << el.info.from.rank << "⇒" << otherRank
<< ", "
<< el.info.from.source << "}"
<< " ∴ {" << el.info.tuple[0]
<< ", "
<< el.info.tuple[1]
<< "}"
<< "\n"
;
WITH_CHRONO("db:io:send",
u.send(otherRank, el, sendTag);
)
} // send phase
} // otherRank
};
#if defined(HAVE_OCD) || defined(ATRIP_PRINT_TUPLES)
std::map<ABCTuple, double> tupleEnergies;
#endif
const double doublesFlops
= double(No)
* double(No)
* double(No)
* (double(No) + double(Nv))
* 2.0
* (traits::isComplex<F>() ? 2.0 : 1.0)
* 6.0
/ 1e9
;
// START MAIN LOOP ======================================================{{{1
double energy(0.);
size_t first_iteration = 0;
Checkpoint c;
const size_t checkpoint_mod
= in.checkpointAtEveryIteration != 0
? in.checkpointAtEveryIteration
: nIterations * in.checkpointAtPercentage / 100;
if (in.readCheckpointIfExists) {
std::ifstream fin(in.checkpointPath);
if (fin.is_open()) {
LOG(0, "Atrip") << "Reading checkpoint from "
<< in.checkpointPath << "\n";
c = read_checkpoint(fin);
first_iteration = (size_t)c.iteration;
if (first_iteration > nIterations) {
// TODO: throw an error here
// first_iteration is bigger than nIterations,
// you probably started the program with a different number
// of cores
}
if (No != c.no) {/* TODO: write warning */}
if (Nv != c.nv) {/* TODO: write warning */}
// TODO write warnings for nrank and so on
if (Atrip::rank == 0) {
// take the negative of the energy to correct for the
// negativity of the equations, the energy in the checkpoint
// should always be the correct physical one.
energy = - (double)c.energy;
}
LOG(0, "Atrip") << "energy from checkpoint "
<< energy << "\n";
LOG(0, "Atrip") << "iteration from checkpoint "
<< first_iteration << "\n";
}
}
for ( size_t
i = first_iteration,
iteration = first_iteration + 1
; i < tuplesList.size()
; i++, iteration++
) {
Atrip::chrono["iterations"].start();
// check overhead from chrono over all iterations
WITH_CHRONO("start:stop", {})
// check overhead of doing a barrier at the beginning
WITH_CHRONO("oneshot-mpi:barrier",
WITH_CHRONO("mpi:barrier",
if (in.barrier) MPI_Barrier(universe);
))
// write checkpoints
if (iteration % checkpoint_mod == 0) {
double globalEnergy = 0;
MPI_Reduce(&energy, &globalEnergy, 1, MPI_DOUBLE, MPI_SUM, 0, universe);
Checkpoint out
= {No,
Nv,
0, // TODO
0, // TODO
- globalEnergy,
iteration - 1,
in.rankRoundRobin};
LOG(0, "Atrip") << "Writing checkpoint\n";
if (Atrip::rank == 0) write_checkpoint(out, in.checkpointPath);
}
// write reporting
if (iteration % iterationMod == 0 || iteration == iteration1Percent) {
if (IterationDescription::descriptor) {
IterationDescription::descriptor({
iteration,
nIterations,
Atrip::chrono["iterations"].count()
});
}
LOG(0,"Atrip")
<< "iteration " << iteration
<< " [" << 100 * iteration / nIterations << "%]"
<< " (" << doublesFlops * iteration / Atrip::chrono["doubles"].count()
<< "GF)"
<< " (" << doublesFlops * iteration / Atrip::chrono["iterations"].count()
<< "GF)"
<< "\n";
// PRINT TIMINGS
if (in.chrono)
for (auto const& pair: Atrip::chrono)
LOG(1, " ") << pair.first << " :: "
<< pair.second.count()
<< std::endl;
}
const ABCTuple abc = isFakeTuple(i)
? tuplesList[tuplesList.size() - 1]
: tuplesList[i]
, *abcNext = i == (tuplesList.size() - 1)
? nullptr
: &tuplesList[i + 1]
;
WITH_CHRONO("with_rank",
WITH_RANK << " :it " << iteration
<< " :abc " << pretty_print(abc)
<< " :abcN "
<< (abcNext ? pretty_print(*abcNext) : "None")
<< "\n";
)
// COMM FIRST DATABASE ================================================{{{1
if (i == first_iteration) {
WITH_RANK << "__first__:first database ............ \n";
const auto db = communicateDatabase(abc, universe);
WITH_RANK << "__first__:first database communicated \n";
WITH_RANK << "__first__:first database io phase \n";
doIOPhase(db);
WITH_RANK << "__first__:first database io phase DONE\n";
WITH_RANK << "__first__::::Unwrapping all slices for first database\n";
for (auto& u: unions) u->unwrapAll(abc);
WITH_RANK << "__first__::::Unwrapping slices for first database DONE\n";
MPI_Barrier(universe);
}
// COMM NEXT DATABASE ================================================={{{1
if (abcNext) {
WITH_RANK << "__comm__:" << iteration << "th communicating database\n";
WITH_CHRONO("db:comm",
const auto db = communicateDatabase(*abcNext, universe);
)
WITH_CHRONO("db:io",
doIOPhase(db);
)
WITH_RANK << "__comm__:" << iteration << "th database io phase DONE\n";
}
// COMPUTE DOUBLES ===================================================={{{1
OCD_Barrier(universe);
if (!isFakeTuple(i)) {
WITH_RANK << iteration << "-th doubles\n";
WITH_CHRONO("oneshot-unwrap",
WITH_CHRONO("unwrap",
WITH_CHRONO("unwrap:doubles",
for (auto& u: decltype(unions){&abph, &hhha, &taphh, &tabhh}) {
u->unwrapAll(abc);
}
)))
WITH_CHRONO("oneshot-doubles",
WITH_CHRONO("doubles",
doublesContribution<F>( abc, (size_t)No, (size_t)Nv
// -- VABCI
, abph.unwrapSlice(Slice<F>::AB, abc)
, abph.unwrapSlice(Slice<F>::AC, abc)
, abph.unwrapSlice(Slice<F>::BC, abc)
, abph.unwrapSlice(Slice<F>::BA, abc)
, abph.unwrapSlice(Slice<F>::CA, abc)
, abph.unwrapSlice(Slice<F>::CB, abc)
// -- VHHHA
, hhha.unwrapSlice(Slice<F>::A, abc)
, hhha.unwrapSlice(Slice<F>::B, abc)
, hhha.unwrapSlice(Slice<F>::C, abc)
// -- TA
, taphh.unwrapSlice(Slice<F>::A, abc)
, taphh.unwrapSlice(Slice<F>::B, abc)
, taphh.unwrapSlice(Slice<F>::C, abc)
// -- TABIJ
, tabhh.unwrapSlice(Slice<F>::AB, abc)
, tabhh.unwrapSlice(Slice<F>::AC, abc)
, tabhh.unwrapSlice(Slice<F>::BC, abc)
// -- TIJK
#if defined(HAVE_CUDA)
, (DataFieldType<F>*)Tijk
#else
, Tijk.data()
#endif
);
WITH_RANK << iteration << "-th doubles done\n";
))
}
// COMPUTE SINGLES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% {{{1
OCD_Barrier(universe);
if (!isFakeTuple(i)) {
WITH_CHRONO("oneshot-unwrap",
WITH_CHRONO("unwrap",
WITH_CHRONO("unwrap:singles",
abhh.unwrapAll(abc);
)))
WITH_CHRONO("reorder",
#pragma acc parallel
for (size_t I(0); I < No*No*No; I++)
((DataFieldType<F>*)Zijk)[I] = ((DataFieldType<F>*)Tijk)[I];
)
WITH_CHRONO("singles",
singlesContribution<F>( No, Nv, abc
#if defined(HAVE_CUDA)
, (F*)Tai
#else
, Tai.data()
#endif
, (F*)abhh.unwrapSlice(Slice<F>::AB, abc)
, (F*)abhh.unwrapSlice(Slice<F>::AC, abc)
, (F*)abhh.unwrapSlice(Slice<F>::BC, abc)
#if defined(HAVE_CUDA)
, (F*)Zijk);
#else
, Zijk.data());
#endif
)
}
// COMPUTE ENERGY %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% {{{1
if (!isFakeTuple(i)) {
double tupleEnergy(0.);
int distinct(0);
if (abc[0] == abc[1]) distinct++;
if (abc[1] == abc[2]) distinct--;
const F epsabc(_epsa[abc[0]] + _epsa[abc[1]] + _epsa[abc[2]]);
WITH_CHRONO("energy",
if ( distinct == 0)
tupleEnergy = getEnergyDistinct<F>(epsabc, No, (F*)epsi, (F*)Tijk, (F*)Zijk);
else
tupleEnergy = getEnergySame<F>(epsabc, No, (F*)epsi, (F*)Tijk, (F*)Zijk);
)
#if defined(HAVE_OCD) || defined(ATRIP_PRINT_TUPLES)
tupleEnergies[abc] = tupleEnergy;
#endif
energy += tupleEnergy;
}
// TODO: remove this
if (isFakeTuple(i)) {
// fake iterations should also unwrap whatever they got
WITH_RANK << iteration
<< "th unwrapping because of fake in "
<< i << "\n";
for (auto& u: unions) u->unwrapAll(abc);
}
#ifdef HAVE_OCD
for (auto const& u: unions) {
WITH_RANK << "__dups__:"
<< iteration
<< "-th n" << u->name << " checking duplicates\n";
u->checkForDuplicates();
}
#endif
// CLEANUP UNIONS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%{{{1
OCD_Barrier(universe);
if (abcNext) {
WITH_RANK << "__gc__:" << iteration << "-th cleaning up.......\n";
for (auto& u: unions) {
u->unwrapAll(abc);
WITH_RANK << "__gc__:n" << u->name << " :it " << iteration
<< " :abc " << pretty_print(abc)
<< " :abcN " << pretty_print(*abcNext)
<< "\n";
// for (auto const& slice: u->slices)
// WITH_RANK << "__gc__:guts:" << slice.info << "\n";
u->clearUnusedSlicesForNext(*abcNext);
WITH_RANK << "__gc__: checking validity\n";
#ifdef HAVE_OCD
// check for validity of the slices
for (auto type: u->sliceTypes) {
auto tuple = Slice<F>::subtupleBySlice(abc, type);
for (auto& slice: u->slices) {
if ( slice.info.type == type
&& slice.info.tuple == tuple
&& slice.isDirectlyFetchable()
) {
if (slice.info.state == Slice<F>::Dispatched)
throw std::domain_error( "This slice should not be undispatched! "
+ pretty_print(slice.info));
}
}
}
#endif
}
}
WITH_RANK << iteration << "-th cleaning up....... DONE\n";
Atrip::chrono["iterations"].stop();
// ITERATION END %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%{{{1
}
// END OF MAIN LOOP
MPI_Barrier(universe);
// PRINT TUPLES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%{{{1
#if defined(HAVE_OCD) || defined(ATRIP_PRINT_TUPLES)
LOG(0,"Atrip") << "tuple energies" << "\n";
for (size_t i = 0; i < np; i++) {
MPI_Barrier(universe);
for (auto const& pair: tupleEnergies) {
if (i == rank)
std::cout << pair.first[0]
<< " " << pair.first[1]
<< " " << pair.first[2]
<< std::setprecision(15) << std::setw(23)
<< " tupleEnergy: " << pair.second
<< "\n"
;
}
}
#endif
// COMMUNICATE THE ENERGIES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%{{{1
LOG(0,"Atrip") << "COMMUNICATING ENERGIES \n";
double globalEnergy = 0;
MPI_Reduce(&energy, &globalEnergy, 1, MPI_DOUBLE, MPI_SUM, 0, universe);
WITH_RANK << "local energy " << energy << "\n";
LOG(0, "Atrip") << "Energy: "
<< std::setprecision(15) << std::setw(23)
<< (- globalEnergy) << std::endl;
// PRINT TIMINGS {{{1
if (in.chrono)
for (auto const& pair: Atrip::chrono)
LOG(0,"atrip:chrono") << pair.first << " "
<< pair.second.count() << std::endl;
LOG(0, "atrip:flops(doubles)")
<< nIterations * doublesFlops / Atrip::chrono["doubles"].count() << "\n";
LOG(0, "atrip:flops(iterations)")
<< nIterations * doublesFlops / Atrip::chrono["iterations"].count() << "\n";
// TODO: change the sign in the getEnergy routines
return { - globalEnergy };
}
// instantiate
template Atrip::Output Atrip::run(Atrip::Input<double> const& in);
template Atrip::Output Atrip::run(Atrip::Input<Complex> const& in);
11 Debug and Logging¶
11.1 Macros¶
#pragma once
#include <functional>
#define ATRIP_BENCHMARK
//#define ATRIP_DONT_SLICE
#ifndef ATRIP_DEBUG
# define ATRIP_DEBUG 1
#endif
//#define ATRIP_WORKLOAD_DUMP
#define ATRIP_USE_DGEMM
//#define ATRIP_PRINT_TUPLES
#ifndef ATRIP_DEBUG
#define ATRIP_DEBUG 1
#endif
#if ATRIP_DEBUG == 4
# pragma message("WARNING: You have OCD debugging ABC triples " \
"expect GB of output and consult your therapist")
# include <dbg.h>
# define HAVE_OCD
# define OCD_Barrier(com) MPI_Barrier(com)
# define WITH_OCD
# define WITH_ROOT if (atrip::Atrip::rank == 0)
# define WITH_SPECIAL(r) if (atrip::Atrip::rank == r)
# define WITH_RANK std::cout << atrip::Atrip::rank << ": "
# define WITH_CRAZY_DEBUG
# define WITH_DBG
# define DBG(...) dbg(__VA_ARGS__)
#elif ATRIP_DEBUG == 3
# pragma message("WARNING: You have crazy debugging ABC triples," \
" expect GB of output")
# include <dbg.h>
# define OCD_Barrier(com)
# define WITH_OCD if (false)
# define WITH_ROOT if (atrip::Atrip::rank == 0)
# define WITH_SPECIAL(r) if (atrip::Atrip::rank == r)
# define WITH_RANK std::cout << atrip::Atrip::rank << ": "
# define WITH_CRAZY_DEBUG
# define WITH_DBG
# define DBG(...) dbg(__VA_ARGS__)
#elif ATRIP_DEBUG == 2
# pragma message("WARNING: You have some debugging info for ABC triples")
# define OCD_Barrier(com)
# define WITH_OCD if (false)
# define WITH_ROOT if (atrip::Atrip::rank == 0)
# define WITH_SPECIAL(r) if (atrip::Atrip::rank == r)
# define WITH_RANK std::cout << atrip::Atrip::rank << ": "
# define WITH_CRAZY_DEBUG if (false)
# define WITH_DBG
# define DBG(...) dbg(__VA_ARGS__)
#else
# define OCD_Barrier(com)
# define WITH_OCD if (false)
# define WITH_ROOT if (false)
# define WITH_SPECIAL(r) if (false)
# define WITH_RANK if (false) std::cout << atrip::Atrip::rank << ": "
# define WITH_DBG if (false)
# define WITH_CRAZY_DEBUG if (false)
# define DBG(...)
#endif
And users of the library can redefine the LOG macro
which in case of not being defined is defined as follows:
#ifndef LOG
#define LOG(level, name) if (Atrip::rank == 0) std::cout << name << ": "
#endif
Furthermore, if you do not wish to see any output from ATRIP, simply
define ATRIP_NO_OUTPUT
#ifdef ATRIP_NO_OUTPUT
# undef LOG
# define LOG(level, name) if (false) std::cout << name << ": "
#endif
11.2 Iteration informer¶
In general a code writer will want to write some messages in every iteration. A developer then can register a function to be used in this sense. The input of the function is an IterationDescriptor structure and the output should be nothing.
namespace atrip {
struct IterationDescription;
using IterationDescriptor = std::function<void(IterationDescription const&)>;
struct IterationDescription {
static IterationDescriptor descriptor;
size_t currentIteration;
size_t totalIterations;
double currentElapsedTime;
};
void registerIterationDescriptor(IterationDescriptor);
}
12 Checkpoints and restarts¶
12.1 Introduction¶
For very heavy workloads and possible bugs in the packages it is often useful to restart from a given state of the calculation.
An advantage of the atrip algorithm is that the state is essentially given
by the
No: number of occupied orbitals
Nv: number of virtual orbitals
Nranks: number of ranks
Nnodes: number of nodes
Energy: the current total energy of the iterations
Iteration: the iteration number
Distribution: the type of distribution
RankRoundRobin: wether the round robin is done through the ranks or
nodes
This information we can encode in a simple struct
// template <typename F>
struct Checkpoint {
size_t no, nv;
size_t nranks;
size_t nnodes;
double energy;
size_t iteration;
// TODO
// Input<F>::TuplesDistribution distribution(GROUP_AND_SORT);
bool rankRoundRobin;
};
12.2 Input and output¶
In order to read and write the checkpoint information, we need to define a format. We choose a simple yaml format without any kind of depth, so that we can write quite easily a parser.
void write_checkpoint(Checkpoint const& c, std::string const& filepath) {
std::ofstream out(filepath);
out << "No: " << c.no
<< "\n"
<< "Nv: " << c.nv
<< "\n"
<< "Nranks: " << c.nranks
<< "\n"
<< "Nnodes: " << c.nnodes
<< "\n"
<< "Energy: " << std::setprecision(19) << c.energy
<< "\n"
<< "Iteration: " << c.iteration
<< "\n"
<< "RankRoundRobin: " << (c.rankRoundRobin ? "true" : "false")
<< "\n";
}
Checkpoint read_checkpoint(std::ifstream& in) {
Checkpoint c;
// trim chars from the string, to be more sure and not use regexes
auto trim = [](std::string& s, std::string const& chars) {
s.erase(0, s.find_first_not_of(chars));
s.erase(s.find_last_not_of(chars) + 1);
return s;
};
for (std::string header, value; std::getline(in, header, ':');) {
std::getline(in, value, '\n');
trim(value, " \t"); // trim all whitespaces
trim(header, " \t");
/**/ if (header == "No") c.no = std::atoi(value.c_str());
else if (header == "Nv") c.nv = std::atoi(value.c_str());
else if (header == "Nranks") c.nranks = std::atoi(value.c_str());
else if (header == "Nnodes") c.nnodes = std::atoi(value.c_str());
else if (header == "Energy") c.energy = std::atof(value.c_str());
else if (header == "Iteration") c.iteration = std::atoi(value.c_str());
else if (header == "RankRoundRobin") c.rankRoundRobin = (value[0] == 't');
}
return c;
}
Checkpoint read_checkpoint(std::string const& filepath) {
std::ifstream in(filepath);
return read_checkpoint(in);
}
13 Miscellaneous¶
13.1 Complex numbers¶
#pragma once
#include <complex>
#include <mpi.h>
#include "config.h"
#if defined(HAVE_CUDA)
#include <cuComplex.h>
#endif
namespace atrip {
using Complex = std::complex<double>;
template <typename F> F maybeConjugate(const F);
#if defined(HAVE_CUDA)
cuDoubleComplex& operator+=(cuDoubleComplex& lz, cuDoubleComplex const& rz);
#endif
namespace traits {
template <typename FF> bool isComplex();
namespace mpi {
template <typename F> MPI_Datatype datatypeOf(void);
}
}
}
#include <atrip/Complex.hpp>
namespace atrip {
template <> double maybeConjugate(const double a) { return a; }
template <> Complex maybeConjugate(const Complex a) { return std::conj(a); }
#if defined(HAVE_CUDA)
/*
__device__
template <> double2
maybeConjugate(const double2 a) {
return {a.x, -a.y};
}
*/
__device__
template <> cuDoubleComplex
maybeConjugate(const cuDoubleComplex a) {
return {a.x, -a.y};
}
/*
__device__
double2& operator+=(double2& lz, double2 const& rz) {
lz.x += rz.x;
lz.y += rz.y;
return lz;
}
*/
__device__
cuDoubleComplex& operator+=(cuDoubleComplex& lz, cuDoubleComplex const& rz) {
lz.x += rz.x;
lz.y += rz.y;
return lz;
}
#endif
namespace traits {
template <typename F> bool isComplex() { return false; }
template <> bool isComplex<double>() { return false; }
template <> bool isComplex<Complex>() { return true; }
namespace mpi {
template <> MPI_Datatype datatypeOf<double>() { return MPI_DOUBLE; }
template <> MPI_Datatype datatypeOf<Complex>() { return MPI_DOUBLE_COMPLEX; }
}
}
}
14 Include header¶
#pragma once
#include <atrip/Atrip.hpp>