-
Notifications
You must be signed in to change notification settings - Fork 1
/
nvml_cublas.cu
159 lines (131 loc) · 6.63 KB
/
nvml_cublas.cu
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
/*
* Copyright 1993-2019 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO USER:
*
* This source code is subject to NVIDIA ownership rights under U.S. and
* international Copyright laws. Users and possessors of this source code
* are hereby granted a nonexclusive, royalty-free license to use this code
* in individual and commercial software.
*
* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH
* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
* OR PERFORMANCE OF THIS SOURCE CODE.
*
* U.S. Government End Users. This source code is a "commercial item" as
* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of
* "commercial computer software" and "commercial computer software
* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)
* and is provided to the U.S. Government only as a commercial end item.
* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
* source code with only those rights set forth herein.
*
* Any use of this source code in individual and commercial software must
* include, in the user documentation and internal comments to the code,
* the above Disclaimer and U.S. Government End Users Notice.
*/
/* This example demonstrates how to use NVML library with
* C++11 multithreading to create GPU monitoring with a
* high sampling rate by storing nvidia-smi data to RAM
* and then writing the output to a file once computation
* is complete.
*/
/* Includes, system */
#include <cstdio>
#include <cstdlib>
#include <string>
/* Includes, cuda */
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
/* Includes, custom */
#include "nvmlClass.h"
// *************** FOR ERROR CHECKING *******************
#ifndef CUDA_RT_CALL
#define CUDA_RT_CALL( call ) \
{ \
auto status = static_cast<cudaError_t>( call ); \
if ( status != cudaSuccess ) \
fprintf( stderr, \
"ERROR: CUDA RT call \"%s\" in line %d of file %s failed " \
"with " \
"%s (%d).\n", \
#call, \
__LINE__, \
__FILE__, \
cudaGetErrorString( status ), \
status ); \
}
#endif // CUDA_RT_CALL
// *************** FOR ERROR CHECKING *******************
void calculate( int const &m, int const &n, int const &k, nvmlClass &nvml ) {
float const alpha { 1.0f };
float const beta { 0.0f };
int const lda { m };
int const ldb { k };
int const ldc { m };
size_t const sizeA { static_cast<long unsigned int>( m * k ) };
size_t const sizeB { static_cast<long unsigned int>( k * n ) };
size_t const sizeC { static_cast<long unsigned int>( m * n ) };
cublasHandle_t handle;
/* Initialize CUBLAS */
CUDA_RT_CALL( cublasCreate( &handle ) );
/* Initialize CUBLAS */
printf( "cublasSgemm %dx%dx%d test running..\n", m, n, k );
using data_type = float;
/* Allocate host memory for the matrices */
thrust::host_vector<data_type> h_A( sizeA );
thrust::host_vector<data_type> h_B( sizeB );
thrust::host_vector<data_type> h_C( sizeC );
thrust::host_vector<data_type> h_C_ref( sizeC );
/* Fill the matrices with test data */
/* Assume square matrices */
for ( int i = 0; i < m * m; i++ ) {
h_A[i] = std::rand( ) / static_cast<data_type>( RAND_MAX );
h_B[i] = std::rand( ) / static_cast<data_type>( RAND_MAX );
}
/* Create thread to gather GPU stats */
std::thread threadStart( &nvmlClass::getStats,
&nvml ); // threadStart starts running
/* Allocate device memory for the matrices */
thrust::device_vector<data_type> d_A( h_A );
thrust::device_vector<data_type> d_B( h_B );
thrust::device_vector<data_type> d_C( sizeC );
/* Retrieve raw pointer for device data */
data_type *d_A_ptr = thrust::raw_pointer_cast( &d_A[0] );
data_type *d_B_ptr = thrust::raw_pointer_cast( &d_B[0] );
data_type *d_C_ptr = thrust::raw_pointer_cast( &d_C[0] );
/* Performs operation using cublas */
cublasSgemm( handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k, &alpha, d_A_ptr, lda, d_B_ptr, ldb, &beta, d_C_ptr, ldc );
CUDA_RT_CALL( cudaDeviceSynchronize( ) );
/* Allocate host memory for reading back the result from device memory */
h_C = d_C;
/* Create thread to kill GPU stats */
/* Join both threads to main */
std::thread threadKill( &nvmlClass::killThread, &nvml );
threadStart.join( );
threadKill.join( );
/* Shutdown */
CUDA_RT_CALL( cublasDestroy( handle ) );
}
/* Main */
int main( int argc, char **argv ) {
int dev {};
cudaGetDevice( &dev );
CUDA_RT_CALL( cudaSetDevice( dev ) );
std::string const filename = { "data/gpuStats.csv" };
// Create NVML class to retrieve GPU stats
nvmlClass nvml( dev, filename );
for ( int i = 512; i <= 16834; i *= 2 )
calculate( i, i, i, nvml );
return ( EXIT_SUCCESS );
}