I am trying to parallelize Monte Carlo simulation by using OpenCL. I use the MWC64X as a uniform random number generator. The code runs well on different Intel CPUs, since the output of parallel computation is very close to the sequential one.
Using OpenCL device: Intel(R) Xeon(R) CPU E5-2630L v3 @ 1.80GHz
Literal influence running time: 0.029048 seconds r1 seqInfl= 0.4771
Literal influence running time: 0.029762 seconds r2 seqInfl= 0.4771
Literal influence running time: 0.029742 seconds r3 seqInfl= 0.4771
Literal influence running time: 0.02971 seconds ra seqInfl= 0.4771
Literal influence running time: 0.029225 seconds trust1-57 seqInfl= 0.6001
Literal influence running time: 0.04992 seconds trust110-1 seqInfl= 0
Literal influence running time: 0.034636 seconds trust4-57 seqInfl= 0
Literal influence running time: 0.049079 seconds trust57-110 seqInfl= 0
Literal influence running time: 0.024442 seconds trust57-4 seqInfl= 0.8026
Literal influence running time: 0.04946 seconds trust33-1 seqInfl= 0
Literal influence running time: 0.049071 seconds trust57-33 seqInfl= 0
Literal influence running time: 0.053117 seconds trust4-1 seqInfl= 0.1208
Literal influence running time: 0.051642 seconds trust57-1 seqInfl= 0
Literal influence running time: 0.052052 seconds trust57-64 seqInfl= 0
Literal influence running time: 0.052118 seconds trust64-1 seqInfl= 0
Literal influence running time: 0.051998 seconds trust57-7 seqInfl= 0
Literal influence running time: 0.052069 seconds trust7-1 seqInfl= 0
Total number of literals: 17
Sequential influence running time: 0.71728 seconds
Sequential maxInfluence Literal: trust57-4 0.8026
index1= 17 size= 51 dim1_size= 6
sum0:4781 influence0:0.478100 sum2:4781 influence2:0.478100 sum6:0 influence6:0.000000 sum10:0 sum12:0 influence12:0.000000 sum7:0 influence7:0.000000 influence10:0.000000 sum4:5962 influence4:0.596200 sum8:7971 influence8:0.797100 sum1:4781 influence1:0.478100 sum3:4781 influence3:0.478100 sum13:0 influence13:0.000000 sum11:1261 influence11:0.126100 sum9:0 influence9:0.000000 sum14:0 influence14:0.000000 sum5:0 influence5:0.000000 sum15:0 influence15:0.000000 sum16:0 influence16:0.000000
Parallel influence running time: 0.054391 seconds
Parallel maxInfluence Literal: trust57-4 Infl=0.7971
However, when I run the code on GeForce GTX 1080 Ti, with NVIDIA-SMI 430.40 and CUDA 10.1 and OpenCL 1.2 CUDA installed, the output is as below:
Using OpenCL device: GeForce GTX 1080 Ti
Influence:
Literal influence running time: 0.011119 seconds r1 seqInfl= 0.4771
Literal influence running time: 0.011238 seconds r2 seqInfl= 0.4771
Literal influence running time: 0.011408 seconds r3 seqInfl= 0.4771
Literal influence running time: 0.01109 seconds ra seqInfl= 0.4771
Literal influence running time: 0.011132 seconds trust1-57 seqInfl= 0.6001
Literal influence running time: 0.018978 seconds trust110-1 seqInfl= 0
Literal influence running time: 0.013093 seconds trust4-57 seqInfl= 0
Literal influence running time: 0.018968 seconds trust57-110 seqInfl= 0
Literal influence running time: 0.009105 seconds trust57-4 seqInfl= 0.8026
Literal influence running time: 0.018753 seconds trust33-1 seqInfl= 0
Literal influence running time: 0.018583 seconds trust57-33 seqInfl= 0
Literal influence running time: 0.02005 seconds trust4-1 seqInfl= 0.1208
Literal influence running time: 0.01957 seconds trust57-1 seqInfl= 0
Literal influence running time: 0.019686 seconds trust57-64 seqInfl= 0
Literal influence running time: 0.019632 seconds trust64-1 seqInfl= 0
Literal influence running time: 0.019687 seconds trust57-7 seqInfl= 0
Literal influence running time: 0.019859 seconds trust7-1 seqInfl= 0
Total number of literals: 17
Sequential influence running time: 0.272032 seconds
Sequential maxInfluence Literal: trust57-4 0.8026
index1= 17 size= 51 dim1_size= 6
sum0:10000 sum1:10000 sum2:10000 sum3:10000 sum4:10000 sum5:0 sum6:0 sum7:0 sum8:10000 sum9:0 sum10:0 sum11:0 sum12:0 sum13:0 sum14:0 sum15:0 sum16:0
Parallel influence running time: 0.193581 seconds
The "Influence" value equals sum*1.0/10000, thus the parallel influence only composes of 1 and 0, which is incorrect (in GPU runs) and doesn't happen when parallelizing on a Intel CPU.
When I check the output of the random number generator if(flag==0) printf("randint=%u",randint);, it seems the outputs are all zero on GPU. Below is the clinfo and the .cl code:
Device Name GeForce GTX 1080 Ti
Device Vendor NVIDIA Corporation
Device Vendor ID 0x10de
Device Version OpenCL 1.2 CUDA
Driver Version 430.40
Device OpenCL C Version OpenCL C 1.2
Device Type GPU
Device Topology (NV) PCI-E, 68:00.0
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
Max compute units 28
Max clock frequency 1721MHz
Compute Capability (NV) 6.1
Device Partition (core)
Max number of sub-devices 1
Supported partition types None
Max work item dimensions 3
Max work item sizes 1024x1024x64
Max work group size 1024
Preferred work group size multiple 32
Warp size (NV) 32
Preferred / native vector sizes
char 1 / 1
short 1 / 1
int 1 / 1
long 1 / 1
half 0 / 0 (n/a)
float 1 / 1
double 1 / 1 (cl_khr_fp64)
Half-precision Floating-point support (n/a)
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations Yes
Double-precision Floating-point support (cl_khr_fp64)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Address bits 64, Little-Endian
Global memory size 11720130560 (10.92GiB)
Error Correction support No
Max memory allocation 2930032640 (2.729GiB)
Unified memory for Host and Device No
Integrated memory (NV) No
Minimum alignment for any data type 128 bytes
Alignment of base address 4096 bits (512 bytes)
Global Memory cache type Read/Write
Global Memory cache size 458752 (448KiB)
Global Memory cache line size 128 bytes
Image support Yes
Max number of samplers per kernel 32
Max size for 1D images from buffer 134217728 pixels
Max 1D or 2D image array size 2048 images
Max 2D image size 16384x32768 pixels
Max 3D image size 16384x16384x16384 pixels
Max number of read image args 256
Max number of write image args 16
Local memory type Local
Local memory size 49152 (48KiB)
Registers per block (NV) 65536
Max number of constant args 9
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 4352 (4.25KiB)
Queue properties
Out-of-order execution Yes
Profiling Yes
Prefer user sync for interop No
Profiling timer resolution 1000ns
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
Kernel execution timeout (NV) Yes
Concurrent copy and kernel execution (NV) Yes
Number of async copy engines 2
printf() buffer size 1048576 (1024KiB)
#define N 70 // N > index, which is the total number of literals
#define BASE 4294967296UL
//! Represents the state of a particular generator
typedef struct{ uint x; uint c; } mwc64x_state_t;
enum{ MWC64X_A = 4294883355U };
enum{ MWC64X_M = 18446383549859758079UL };
void MWC64X_Step(mwc64x_state_t *s)
{
uint X=s->x, C=s->c;
uint Xn=MWC64X_A*X+C;
uint carry=(uint)(Xn<C); // The (Xn<C) will be zero or one for scalar
uint Cn=mad_hi(MWC64X_A,X,carry);
s->x=Xn;
s->c=Cn;
}
//! Return a 32-bit integer in the range [0..2^32)
uint MWC64X_NextUint(mwc64x_state_t *s)
{
uint res=s->x ^ s->c;
MWC64X_Step(s);
return res;
}
__kernel void setInfluence(const int literals, const int size, const int dim1_size, __global int* lambdas, __global float* lambdap, __global int* dim2_size, __global float* influence){
int flag=get_global_id(0);
int sum=0;
int count=10000;
int assignment[N];
//or try to get newlambda like original version does
if(flag < literals){
mwc64x_state_t rng;
for(int i=0; i<count; i++){
for(int j=0; j<size; j++){
uint randint=MWC64X_NextUint(&rng);
float rand=randint*1.0/BASE;
//if(flag==0)
// printf("randint=%u",randint);
if(lambdap[j]<rand)
assignment[lambdas[j]]=0;
else
assignment[lambdas[j]]=1;
}
//the true case
assignment[flag]=1;
int valuet=0;
int index=0;
for(int m=0; m<dim1_size; m++){
int valueMono=1;
for(int n=0; n<dim2_size[m]; n++){
if(assignment[lambdas[index+n]]==0){
valueMono=0;
index+=dim2_size[m];
break;
}
}
if(valueMono==1){
valuet=1;
break;
}
}
//the false case
assignment[flag]=0;
int valuef=0;
index=0;
for(int m=0; m<dim1_size; m++){
int valueMono=1;
for(int n=0; n<dim2_size[m]; n++){
if(assignment[lambdas[index+n]]==0){
valueMono=0;
index+=dim2_size[m];
break;
}
}
if(valueMono==1){
valuef=1;
break;
}
}
sum += valuet-valuef;
}
influence[flag] = 1.0*sum/count;
printf("sum%d:%d\t", flag, sum);
}
}
What might be the problem when running the code on GPU? Is it MWC64X? According to its author, it can perform well on NVIDIA GPUs. If so, how can I fix it; if not, what might be the problem?
mwc64x_state_t rngvariable, so any results will be undefined. Note that you will probably want to seed your RNG differently for each work-item, otherwise you will get nasty correlation artifacts in your results. – pmdj