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fenglai
Beginner
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ask for helps on improving the TBB parallel efficiency regarding hyper-threading etc.

Hello! I have some confusing parallel timing results for my code, which performs
heavy numerical work.

For using 1 core, my timing is 882 seconds;
For using 8 cores, my timing is 121 seconds (7.3x faster);
for using 16 cores; my timing is 101 seconds (8.7x faster);
for using 32 logical cores; timing is 100 seconds, as same with using 16 cores.

My questions focus on two points:

1  why the timing for hyper-threading is same with 16 physical cores?
2  the 8 cores timing shows nearly linear speedup. However, why 16 cores only
   have marginal improvements against with 8 cores?

The working environment is:
Two 2.6 GHz 64bit Intel 8-core XEON E5-2600 with 16 physical cores and 32 logical cores
32 GB of memory

Finally let me introduce more details about my code. This code is performing
4body analytical integrals in quantum chemistry in terms of Gaussian type
functions.

This is the head file to define the class with operator():
UInt is just size_t type.

   /**
    * \class TBB_GInts4D
    * the result is in form of matrix
    */
   class TBB_GInts4DMatrix {

      private:

         //  
         // basic data needed for calculation
         //  
         bool sameShellPair;                   ///< whether the bra and ket are coming from same source?
         UInt nSpin;                           ///< number of spin states
         Double thresh;                        ///< threshold value for gints4d work
         Double spThresh;                      ///< threshold value for generating shell pairs

         //  
         // reference for the input
         //  
         all of data here are constant reference.....

         //  
         // the output data
         //  
         Mtrx alphaMatrix;                     ///< alpha  matrix
         Mtrx betaMatrix;                      ///< beta  matrix

      public:

         /**
          * constructor
          * \param spStatus     whether bra and ket are same shell pair infor?
          * \param ginfor       information center for gints job
          * \param bra,ket      shell pair infor class for bra and ket side
          * \param b1,b2,k1,k2  reference for shell data
          * \param alpha,beta   input density matrices
          */
         TBB_GInts4DMatrix(bool spStatus, const GIntJobInfor& ginfor0, const SigMolShellPairInfor& bra,
              const SigMolShellPairInfor& ket, const MolShell& b1, const MolShell& b2,
              const MolShell& k1, const MolShell& k2, const Mtrx& alpha, const Mtrx& beta);
    
         /**
          * destructor
          */
         ~TBB_GInts4DMatrix() { };

         /**
          * functional operator to generate the integral results
          * this is used as parallel_reduce
          */
         void operator()(const blocked_range<UInt>& r);

         /**
          * splitting constructor used by TBB
          * we copy all of reference data except result
          * which should be always empty initially
          */
         TBB_GInts4DMatrix(const TBB_GInts4DMatrix& tbb_gints4d, split);

         /**
          * for assemble results in threads together
          */
         void join(TBB_GInts4DMatrix& tbb_gints4d) {   

            // update the alpha/beta matrix
            alphaMatrix.add(tbb_gints4d.alphaMatrix);
            if (nSpin == 2) {
               betaMatrix.add(tbb_gints4d.betaMatrix);
            }   
         };

         .......
      }


This is the cpp file for this class:

// constructor
TBB_GInts4DMatrix::TBB_GInts4DMatrix(bool spStatus, const GIntJobInfor& ginfor0,
      const SigMolShellPairInfor& bra, const SigMolShellPairInfor& ket,
      const MolShell& b1, const MolShell& b2, const MolShell& k1, const MolShell& k2,
      const Mtrx& alpha, const Mtrx& beta):sameShellPair(spStatus),nSpin(ginfor0.getNSpin()),
   thresh(ginfor0.int4DThreshVal()),spThresh(ginfor0.spThreshVal()),
   bra1(b1),bra2(b2),ket1(k1),ket2(k2),braInfo(bra),ketInfo(ket),
   ginfor(ginfor0),alphaDen(alpha),betaDen(beta)
{
   // set up results
   alphaMatrix.init(alphaDen.getRow(),alphaDen.getCol());
   if (nSpin==2) {
      betaMatrix.init(betaDen.getRow(),betaDen.getCol());
   }   
}

// copy constructor
TBB_GInts4DMatrix::TBB_GInts4DMatrix(const TBB_GInts4DMatrix& tbb_gints4d,
      split):sameShellPair(tbb_gints4d.sameShellPair),
   nSpin(tbb_gints4d.nSpin),thresh(tbb_gints4d.thresh),spThresh(tbb_gints4d.spThresh),
   bra1(tbb_gints4d.bra1),bra2(tbb_gints4d.bra2),ket1(tbb_gints4d.ket1),
   ket2(tbb_gints4d.ket2),braInfo(tbb_gints4d.braInfo),ketInfo(tbb_gints4d.ketInfo),
   ginfor(tbb_gints4d.ginfor),alphaDen(tbb_gints4d.alphaDen),betaDen(tbb_gints4d.betaDen)
{
   // set up results
   alphaMatrix.init(alphaDen.getRow(),alphaDen.getCol());
   if (nSpin==2) {
      betaMatrix.init(betaDen.getRow(),betaDen.getCol());
   }   
}

// this is the body for defining operator ()
void TBB_GInts4DMatrix::operator()(const blocked_range<UInt>& r)  
{
   //  
   // setup scratch data for ERI calculation
   // 1  atom shell pair (bra and ket)
   // 2  heap memory management if necessary
   // 3  integral array
   // 4  J/K digestion
   //  
   basically, here I just allocate memory for scratch data
   in terms of TBB scalable allocator with STL vector

   // now real work begins
   for( UInt n=r.begin(); n!=r.end(); ++n ) {
          
         // here we perform heavy numerical work to calculate
         // the 4 body integrals. All of variables are local
         // to threads, no more memory needed except above
         // scratch data
         // the calculation inside the loop costs 10^5 to 10^6 CPU
         // instructions to finish
         // the results will be added to result alphaMatrix and
         // betaMatrix, which are local per thread
   }
}

This is the way I use the class:

   // get the global information
   const GlobalInfor& globInfor = ginfor.getGlobalInfor();

   // possible timing code
   tick_count t0 = tick_count::now();

   // set up the scheduler
   task_scheduler_init init(task_scheduler_init::deferred);
   if (globInfor.useMultiThreads()) {
      init.initialize();
   }else{
      init.initialize(1);
   }

   // do normal integral matrix calculation
   UInt braLen = braInfor.getNSigAtomShellPairs();
   bool sameShellPairs = true;
   UInt len = braLen*(braLen+1)/2;
   TBB_GInts4DMatrix intWork(sameShellPairs,ginfor,braInfor,ketInfor,s,s,s,s,den.getMtrx(0),den.getMtrx(1));
   parallel_reduce(blocked_range<UInt>(0,len),intWork);

   // possible timing code
   tick_count t1 = tick_count::now();
   Double t = (t1-t0).seconds();
   if (globInfor.printTiming()) {
      printf("%s  %-12.6f\n", "GInts4D work with TBB threads, time in seconds  ", t);
   }


Thank you so much for your help!!!

fenglai
 

 

0 Kudos
6 Replies
fenglai
Beginner
30 Views

For bettering understanding the situation, here is what I have done for exploring the reason behind
my questions.

   I have used the Intel VTune to investigate the hot-spots inside the program. All of expensive
   functions called are within the loop of
   for( UInt n=r.begin(); n!=r.end(); ++n ) {
        ......
   }  
   in the operator() function. The top expensive functions are like:
   _exp (Exponential function from libm.so);
   _erf (error function from libm.so);
   _pow (power function from libm.so);
   _dgemv(from mkl);
   .....

   From the report, the overhead and the "spin time"(seems to be the synchronization cost made by TBB)
   are only marginal comparing with the timing inside the operator(). It takes less than %1 of the total
   elapsed time.
   
   so the situation seems indicating that this code is perfectly fitting for parallel since it's overhead
   cost etc. are very small. However, why we have such low parallel efficiency for 16 and 32 cores?

fenglai
Beginner
30 Views

------------------------------------

my previous post:

For using 1 core, my timing is 882 seconds;
For using 8 cores, my timing is 121 seconds (7.3x faster);
for using 16 cores; my timing is 101 seconds (8.7x faster);
for using 32 logical cores; timing is 100 seconds, as same with using 16 cores.

My questions focus on two points:

1  why the timing for hyper-threading is same with 16 physical cores?
2  the 8 cores timing shows nearly linear speedup. However, why 16 cores only
   have marginal improvements against with 8 cores?

.............................................

My friend gave me a suggestion for understanding the thing I had. He suggested that because my code is doing
heavy numerical work therefore it's "CPU Bound", hyper-threading will not work in this case. So this explains
why 32 logical cores gives the nearly same performance comparing with 16 physical cores situation.

Secondly, he guess that because physically each Intel 8-core XEON E5-2600 is in a socket of motherboard;
the cores inside one socket are communicating with L1 cache, and the cores between different sockets are
communicating with L2 cache. Therefore for heavy load job when the work-stealing algorithm is performed;
the TBB is going to be hurt by the slow communicating between cores in different sockets.

I do not know whether his guess are reasonable?

 

fenglai
Beginner
30 Views

Finally let me just pull off my final thread, then I can go to bed:)

I also tested the calculation on another workstation(job is a bit of different, therefore timing is not same
with the one above).

This machine has 128GB memory and CPU is one E5-1650 with 3.2G 6 physical cores. So I have 12 logical cores.

The timing is like this:

1 core timing is 566 seconds;
6 cores timing is 102 seconds(5.5X faster);
12 cores timing is 82 seconds(6.9X faster)

 

RafSchietekat
Black Belt
30 Views

"My friend gave me a suggestion for understanding the thing I had. He suggested that because my code is doing heavy numerical work therefore it's "CPU Bound", hyper-threading will not work in this case. So this explains why 32 logical cores gives the nearly same performance comparing with 16 physical cores situation."

I would also expect that.

"Secondly, he guess that because physically each Intel 8-core XEON E5-2600 is in a socket of motherboard; the cores inside one socket are communicating with L1 cache, and the cores between different sockets are communicating with L2 cache. Therefore for heavy load job when the work-stealing algorithm is performed; the TBB is going to be hurt by the slow communicating between cores in different sockets."

TBB tries to avoid stealing because there's always a cost, even without NUMA, doing just about what's right for load balancing. Since you're using a predefined algorithm, you should be fine there. It can't fully absorb the extra overhead of remote communication, of course, but this wouldn't even begin to explain what you are seeing here.

fenglai
Beginner
30 Views

Raf Schietekat wrote:

"My friend gave me a suggestion for understanding the thing I had. He suggested that because my code is doing heavy numerical work therefore it's "CPU Bound", hyper-threading will not work in this case. So this explains why 32 logical cores gives the nearly same performance comparing with 16 physical cores situation."

I would also expect that.

"Secondly, he guess that because physically each Intel 8-core XEON E5-2600 is in a socket of motherboard; the cores inside one socket are communicating with L1 cache, and the cores between different sockets are communicating with L2 cache. Therefore for heavy load job when the work-stealing algorithm is performed; the TBB is going to be hurt by the slow communicating between cores in different sockets."

TBB tries to avoid stealing because there's always a cost, even without NUMA, doing just about what's right for load balancing. Since you're using a predefined algorithm, you should be fine there. It can't fully absorb the extra overhead of remote communication, of course, but this wouldn't even begin to explain what you are seeing here.

Thank you Ref for your help!

I will try to provide more details about the grainsize and other partitioners to see whether I can get a clue. 

fenglai

30 Views

fenglai wrote:

   in the operator() function. The top expensive functions are like:

   _exp (Exponential function from libm.so);
   _erf (error function from libm.so);
   _pow (power function from libm.so);
   _dgemv(from mkl);
   .....

   cost etc. are very small. However, why we have such low parallel efficiency for 16 and 32 cores?

Did you use intel compiler in addition to MKL? I wondering why non-vector functions from libm.so are used instead of vectorized funtions from compiler runtime.

fenglai wrote:

For using 1 core, my timing is 882 seconds;
For using 8 cores, my timing is 121 seconds (7.3x faster);
for using 16 cores; my timing is 101 seconds (8.7x faster);
for using 32 logical cores; timing is 100 seconds, as same with using 16 cores.

1  why the timing for hyper-threading is same with 16 physical cores?
2  the 8 cores timing shows nearly linear speedup. However, why 16 cores only
   have marginal improvements against with 8 cores?

Linux scheduler might schedule all 16 threads to be run on one socket in case there is NUMA awareness in the system. you can check this running your sample via taskset command.

--Vladimir