RECENT DEVELOPMENT IN COMPUTATIONAL SCIENCE

ISCS 2011 Selected Papers Vol.2 Time Benchmarks for the OpenMP and GPU
GFLOPS
GFLOPS
350
300
250
200
150
100
50
0
300
250
200
150
100
50
0
(a) real number (M2)
DGEMM(8 Threads)
DGEMM(Tesla C1060)
SGEMM(Tesla C1060)
Ratio(Single/Double)
256
512
1024 2048
Matrix size
4096
(c) real number (M3)
Tesla C1060
Tesla M2050 with cuda3.1
Tesla M2050 with cuda3.2
256
512
1024 2048
Matrix size
4096
5120
5120
7
6
5
4
3
2
1
0
RATIO
GFLOPS
GFLOPS
350
300
250
200
150
100
50
0
300
250
200
150
100
50
0
(b) complex number (M2)
ZGEMM(8 Threads)
ZGEMM(Tesla C1060)
CGEMM(Tesla C1060)
Ratio(Single/Double)
256
512
1024 2048
Matrix size
4096
5120
(d) complex number (M3)
Tesla C1060
Tesla M2050 with cuda3.1
Tesla M2050 with cuda3.2
256
512 1024
Matrix size
2048
Figure 4: Effective FLOPS values of the MM for various matrix sizes (256 ∼ 5120) by SGEMM, CGEMM, DGEMM
and ZGEMM routines in Tesla GPUs (Tesla C1060 and Tesla M2050). The upper and lower two panels are obtained
in the machines of M2 (a)(b) and M3 (c)(d) (see Table 1), respectively. The times for memory transfer are included
in the estimation.
Memory Transfer Rate [%]
100
80
60
40
20
0
MM
DtoH
HtoD
37.5%
14.6%
47.8%
128
51.8%
11.3%
36.8%
256
56.9%
20.1%
22.9%
1024
79.2%
8.6%
12.2%
4096
Figure 5: Data transfer ratio in DGEMM calculation for
various matrix sizes in M3.
21
4096
6
4
2
0
RATIO