Commit 09f22251 authored by Dmitry I. Lyakh's avatar Dmitry I. Lyakh
Browse files

Added exatn::getMemoryUsage() API, there is a tensor buffer leak ... 



Signed-off-by: default avatarDmitry I. Lyakh <quant4me@gmail.com>
parent 2808f6f1

src/exatn/exatn_numerics.hpp

+7 −1
Original line number Diff line number Diff line 
/** ExaTN::Numerics: General client header (free function API)

REVISION: 2022/01/08

REVISION: 2022/01/17



Copyright (C) 2018-2022 Dmitry I. Lyakh (Liakh)

Copyright (C) 2018-2022 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -1153,6 +1153,12 @@ inline std::size_t getMemoryBufferSize() 
 {return numericalServer->getMemoryBufferSize();}





/** Returns the current memory usage by all allocated tensors.

    Note that the returned value includes buffer fragmentation overhead. **/

inline std::size_t getMemoryUsage(std::size_t * free_mem)

 {return numericalServer->getMemoryUsage(free_mem);}





/** Returns the current value of the Flop counter. **/

inline double getTotalFlopCount()

 {return numericalServer->getTotalFlopCount();}

src/exatn/num_server.cpp

+7 −1
Original line number Diff line number Diff line 
/** ExaTN::Numerics: Numerical server

REVISION: 2022/01/14

REVISION: 2022/01/17



Copyright (C) 2018-2022 Dmitry I. Lyakh (Liakh)

Copyright (C) 2018-2022 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -309,6 +309,12 @@ std::size_t NumServer::getMemoryBufferSize() const 
 return tensor_rt_->getMemoryBufferSize();

}



std::size_t NumServer::getMemoryUsage(std::size_t * free_mem) const

{

 while(!tensor_rt_);

 return tensor_rt_->getMemoryUsage(free_mem);

}



double NumServer::getTotalFlopCount() const

{

 while(!tensor_rt_);

src/exatn/num_server.hpp

+5 −1
Original line number Diff line number Diff line 
/** ExaTN::Numerics: Numerical server

REVISION: 2022/01/07

REVISION: 2022/01/17



Copyright (C) 2018-2022 Dmitry I. Lyakh (Liakh)

Copyright (C) 2018-2022 Oak Ridge National Laboratory (UT-Battelle) **/
@@ -306,6 +306,10 @@ public: 
 /** Returns the Host memory buffer size in bytes provided by the runtime. **/

 std::size_t getMemoryBufferSize() const;



 /** Returns the current memory usage by all allocated tensors.

     Note that the returned value includes buffer fragmentation overhead. **/

 std::size_t getMemoryUsage(std::size_t * free_mem) const;



 /** Returns the current value of the Flop counter. **/

 double getTotalFlopCount() const;

src/exatn/tests/NumServerTester.cpp

+45 −45
Original line number Diff line number Diff line
@@ -18,10 +18,10 @@ 
#include "errors.hpp"



//Test activation:

/*



#define EXATN_TEST0

#define EXATN_TEST1

#define EXATN_TEST2

/*#define EXATN_TEST2

#define EXATN_TEST3

#define EXATN_TEST4

#define EXATN_TEST5
@@ -40,19 +40,19 @@ 
#define EXATN_TEST18

#define EXATN_TEST19

#define EXATN_TEST20

#define EXATN_TEST21

#define EXATN_TEST21*/

#define EXATN_TEST22

#define EXATN_TEST23

#define EXATN_TEST24

#define EXATN_TEST25

#define EXATN_TEST26

//#define EXATN_TEST27 //requires input file from source

//#define EXATN_TEST28 //requires input file from source

#define EXATN_TEST27 //requires input file from source

#define EXATN_TEST28 //requires input file from source

#define EXATN_TEST29

#define EXATN_TEST30*/

//#define EXATN_TEST31 //requires input file from source

#define EXATN_TEST30

#define EXATN_TEST31 //requires input file from source

#define EXATN_TEST32

//#define EXATN_TEST33

#define EXATN_TEST33





#ifdef EXATN_TEST0
@@ -70,7 +70,7 @@ TEST(NumServerTester, PerformanceExaTN) 
                                    //3072 for Maxwell, 4096 for Pascal and Volta

 const auto TENS_ELEM_TYPE = TensorElementType::REAL32;



 //exatn::resetLoggingLevel(1,2); //debug

 exatn::resetLoggingLevel(1,2); //debug



 //exatn::resetExecutionSerialization(true,true); //debug
@@ -273,7 +273,7 @@ TEST(NumServerTester, PerformanceExaTN) 


 //Synchronize ExaTN server:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

}

#endif
@@ -386,7 +386,7 @@ TEST(NumServerTester, ExamplarExaTN) 


 //Synchronize ExaTN server:

 success = exatn::sync(all_processes); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

}

#endif
@@ -476,7 +476,7 @@ TEST(NumServerTester, ParallelExaTN) 


 //All processes: Synchronize ExaTN server:

 success = exatn::sync(all_processes); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

}

#endif
@@ -1554,7 +1554,7 @@ TEST(NumServerTester, IsingTNO) 


 bool success = true;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 //Define Ising Hamiltonian constants:

 constexpr std::complex<double> ZERO{ 0.0, 0.0};
@@ -1765,7 +1765,7 @@ TEST(NumServerTester, IsingTNO) 
  //Synchronize:

  success = exatn::sync(); assert(success);

 }

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

}

#endif
@@ -1930,7 +1930,7 @@ TEST(NumServerTester, testGarbage) { 
 using exatn::TensorNetwork;

 using exatn::TensorElementType;



 //exatn::resetLoggingLevel(2,2); // debug

 //exatn::resetLoggingLevel(1,2); // debug



 // Define the initial qubit state vector:

 std::vector<std::complex<double>> qzero{
@@ -2069,7 +2069,7 @@ TEST(NumServerTester, testHyper) { 
 const auto ltens_val = std::complex<double>{0.001,-0.0001};

 const auto rtens_val = std::complex<double>{0.002,-0.0002};



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2325,7 +2325,7 @@ TEST(NumServerTester, neurIPS) { 
 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::deactivateContrSeqCaching();

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2340,7 +2340,7 @@ TEST(NumServerTester, MPSNorm) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2404,7 +2404,7 @@ TEST(NumServerTester, MPSNorm) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2423,7 +2423,7 @@ TEST(NumServerTester, UserDefinedMethod) { 
 const unsigned int num_virtuals = 36;

 const unsigned int num_total = num_occupied + num_virtuals;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2497,7 +2497,7 @@ TEST(NumServerTester, UserDefinedMethod) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2512,7 +2512,7 @@ TEST(NumServerTester, PrintTensors) { 


 const auto TENS_ELEM_TYPE = TensorElementType::REAL64;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2545,7 +2545,7 @@ TEST(NumServerTester, PrintTensors) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2560,7 +2560,7 @@ TEST(NumServerTester, CollapseTensors) { 


 const auto TENS_ELEM_TYPE = TensorElementType::REAL32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2599,7 +2599,7 @@ TEST(NumServerTester, CollapseTensors) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2615,7 +2615,7 @@ TEST(NumServerTester, Reconstructor) { 


 const auto TENS_ELEM_TYPE = TensorElementType::REAL32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -2683,7 +2683,7 @@ TEST(NumServerTester, Reconstructor) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2700,7 +2700,7 @@ TEST(NumServerTester, OptimizerTransverseIsing) { 


 const auto TENS_ELEM_TYPE = TensorElementType::REAL32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 const int num_sites = 4, max_bond_dim = std::pow(2,num_sites/2);

 double g_factor = 1e-1; // >0.0, 1e0 is critical state
@@ -2824,7 +2824,7 @@ TEST(NumServerTester, OptimizerTransverseIsing) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -2841,7 +2841,7 @@ TEST(NumServerTester, OptimizerHubbard) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3000,7 +3000,7 @@ TEST(NumServerTester, OptimizerHubbard) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3023,7 +3023,7 @@ TEST(NumServerTester, ExaTNGenVisitor) { 
 const int max_layers = (num_sites - 1); //1 less CNOT gates

 bool EVALUATE_FULL_TENSOR = false;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3142,7 +3142,7 @@ TEST(NumServerTester, ExaTNGenVisitor) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3160,7 +3160,7 @@ TEST(NumServerTester, HubbardHamiltonian) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX64;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3258,7 +3258,7 @@ TEST(NumServerTester, HubbardHamiltonian) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3276,7 +3276,7 @@ TEST(NumServerTester, MCVQEHamiltonian) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX64;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3342,7 +3342,7 @@ TEST(NumServerTester, MCVQEHamiltonian) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3360,7 +3360,7 @@ TEST(NumServerTester, TensorOperatorReconstruction) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX64;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3387,7 +3387,7 @@ TEST(NumServerTester, TensorOperatorReconstruction) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3609,7 +3609,7 @@ TEST(NumServerTester, SpinHamiltonians) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3771,7 +3771,7 @@ TEST(NumServerTester, ExcitedMCVQE) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3791,7 +3791,7 @@ TEST(NumServerTester, CuTensorNet) { 


 const int NUM_REPEATS = 10;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -3900,7 +3900,7 @@ TEST(NumServerTester, CuTensorNet) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif
@@ -3918,7 +3918,7 @@ TEST(NumServerTester, TensorComposite) { 


 const auto TENS_ELEM_TYPE = TensorElementType::COMPLEX32;



 //exatn::resetLoggingLevel(2,2); //debug

 //exatn::resetLoggingLevel(1,2); //debug



 bool success = true;
@@ -4023,7 +4023,7 @@ TEST(NumServerTester, TensorComposite) { 


 //Synchronize:

 success = exatn::sync(); assert(success);

 exatn::resetLoggingLevel(0,0);

 //exatn::resetLoggingLevel(0,0);

 //Grab a beer!

}

#endif

src/runtime/executor/graph_executors/lazy/graph_executor_lazy.cpp

+9 −3
Original line number Diff line number Diff line 
/** ExaTN:: Tensor Runtime: Tensor graph executor: Lazy

REVISION: 2022/01/10

REVISION: 2022/01/17



Copyright (C) 2018-2022 Dmitry Lyakh

Copyright (C) 2018-2022 Oak Ridge National Laboratory (UT-Battelle)
@@ -153,8 +153,11 @@ void LazyGraphExecutor::execute(TensorGraph & dag) { 
            if(logging_.load() != 0){

              logfile_ << "Success [" << std::fixed << std::setprecision(6)

                       << exatn::Timer::timeInSecHR(getTimeStampStart()) << "]" << std::endl;

              std::size_t free_mem = 0;

              std::size_t used_mem = this->node_executor_->getMemoryUsage(&free_mem);

              logfile_ << "[" << exatn::Timer::timeInSecHR(getTimeStampStart()) << "]"

                       << " Total Flop count = " << getTotalFlopCount() << std::endl;

                       << " Total Flop count = " << getTotalFlopCount()

                       << "; Memory usage = " << used_mem << ", Free = " << free_mem << std::endl;

#ifdef DEBUG

              logfile_.flush();

#endif
@@ -222,8 +225,11 @@ void LazyGraphExecutor::execute(TensorGraph & dag) { 
            logfile_ << "[" << std::fixed << std::setprecision(6) << exatn::Timer::timeInSecHR(getTimeStampStart())

                     << "](LazyGraphExecutor)[EXEC_THREAD]: Synced tensor operation "

                     << node << ": Opcode = " << static_cast<int>(op->getOpcode()) << std::endl;

            std::size_t free_mem = 0;

            std::size_t used_mem = this->node_executor_->getMemoryUsage(&free_mem);

            logfile_ << "[" << exatn::Timer::timeInSecHR(getTimeStampStart()) << "]"

                       << " Total Flop count = " << getTotalFlopCount() << std::endl;

                     << " Total Flop count = " << getTotalFlopCount()

                     << "; Memory usage = " << used_mem << ", Free = " << free_mem << std::endl;

#ifdef DEBUG

            logfile_.flush();

#endif