# GPU Performance Details: Host PC
Contents: | System Configuration Results for datatype double MTimes (double) Backslash (double) FFT (double) Results for datatype single MTimes (single) Backslash (single) FFT (single)
## System Configuration MATLAB Release: R2016b
Host
Name | Intel(R) Core(TM) i5-6400T CPU @ 2.20GHz
Clock | 2201 MHz
Cache | 1024 KB
NumProcessors | 4
OSType | Windows
OSVersion | Microsoft Windows 10 Home
## Results for MTimes (double) These results show the performance of the GPU or host PC when calculating a matrix multiplication of two NxN real matrices. The number of operations is assumed to be `2*N^3 - N^2`. This calculation is usually compute-bound, i.e. the performance depends mainly on how fast the GPU or host PC can perform floating-point operations.
Raw data for Host PC - MTimes (double)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 64,512 | 0.01 | 7.12
4,096 | 520,192 | 0.02 | 22.95
16,384 | 4,177,920 | 0.09 | 49.09
65,536 | 33,488,896 | 0.50 | 67.10
262,144 | 268,173,312 | 2.95 | 90.79
1,048,576 | 2,146,435,072 | 21.52 | 99.76
4,194,304 | 17,175,674,880 | 146.93 | 116.90
16,777,216 | 137,422,176,256 | 1241.25 | 110.71
67,108,864 | 1,099,444,518,912 | 8720.79 | 126.07
(`N` gigaflops = `Nx109` operations per second)
## Results for Backslash (double) These results show the performance of the GPU or host PC when calculating the matrix left division of an `NxN` matrix with an `Nx1` vector. The number of operations is assumed to be `2/3*N^3 + 3/2*N^2`. This calculation is usually compute-bound, i.e. the performance depends mainly on how fast the GPU or host PC can perform floating-point operations.
Raw data for Host PC - Backslash (double)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 23,381 | 0.02 | 0.97
4,096 | 180,907 | 0.06 | 2.90
16,384 | 1,422,677 | 0.20 | 6.99
65,536 | 11,283,115 | 0.92 | 12.32
262,144 | 89,871,701 | 3.32 | 27.09
1,048,576 | 717,400,747 | 22.26 | 32.22
4,194,304 | 5,732,914,517 | 216.86 | 26.44
16,777,216 | 45,838,150,315 | 875.83 | 52.34
67,108,864 | 366,604,539,221 | 5050.73 | 72.58
(`N` gigaflops = `Nx109` operations per second)
## Results for FFT (double) These results show the performance of the GPU or host PC when calculating the Fast-Fourier-Transform of a vector of complex numbers. The number of operations for a vector of length `N` is assumed to be `5*N*log2(N)`. This calculation is usually memory-bound, i.e. the performance depends mainly on how fast the GPU or host PC can read and write data.
Raw data for Host PC - FFT (double)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 51,200 | 0.01 | 6.84
4,096 | 245,760 | 0.04 | 5.96
16,384 | 1,146,880 | 0.17 | 6.61
65,536 | 5,242,880 | 0.59 | 8.96
262,144 | 23,592,960 | 4.78 | 4.94
1,048,576 | 104,857,600 | 33.34 | 3.15
4,194,304 | 461,373,440 | 151.62 | 3.04
16,777,216 | 2,013,265,920 | 792.33 | 2.54
(`N` gigaflops = `Nx109` operations per second)
## Results for MTimes (single) These results show the performance of the GPU or host PC when calculating a matrix multiplication of two NxN real matrices. The number of operations is assumed to be `2*N^3 - N^2`. This calculation is usually compute-bound, i.e. the performance depends mainly on how fast the GPU or host PC can perform floating-point operations.
Raw data for Host PC - MTimes (single)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 64,512 | 0.00 | 25.13
4,096 | 520,192 | 0.02 | 31.90
16,384 | 4,177,920 | 0.04 | 115.50
65,536 | 33,488,896 | 0.16 | 205.24
262,144 | 268,173,312 | 1.32 | 203.83
1,048,576 | 2,146,435,072 | 9.81 | 218.86
4,194,304 | 17,175,674,880 | 76.02 | 225.93
16,777,216 | 137,422,176,256 | 594.40 | 231.19
67,108,864 | 1,099,444,518,912 | 4048.60 | 271.56
(`N` gigaflops = `Nx109` operations per second)
## Results for Backslash (single) These results show the performance of the GPU or host PC when calculating the matrix left division of an `NxN` matrix with an `Nx1` vector. The number of operations is assumed to be `2/3*N^3 + 3/2*N^2`. This calculation is usually compute-bound, i.e. the performance depends mainly on how fast the GPU or host PC can perform floating-point operations.
Raw data for Host PC - Backslash (single)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 23,381 | 0.02 | 1.03
4,096 | 180,907 | 0.06 | 2.95
16,384 | 1,422,677 | 0.17 | 8.40
65,536 | 11,283,115 | 0.61 | 18.57
262,144 | 89,871,701 | 2.38 | 37.76
1,048,576 | 717,400,747 | 10.23 | 70.14
4,194,304 | 5,732,914,517 | 96.87 | 59.18
16,777,216 | 45,838,150,315 | 414.72 | 110.53
67,108,864 | 366,604,539,221 | 2267.03 | 161.71
(`N` gigaflops = `Nx109` operations per second)
## Results for FFT (single) These results show the performance of the GPU or host PC when calculating the Fast-Fourier-Transform of a vector of complex numbers. The number of operations for a vector of length `N` is assumed to be `5*N*log2(N)`. This calculation is usually memory-bound, i.e. the performance depends mainly on how fast the GPU or host PC can read and write data.
Raw data for Host PC - FFT (single)
Array size (elements) | Num Operations | Time (ms) | GigaFLOPS
--- | --- | --- | ---
1,024 | 51,200 | 0.01 | 9.12
4,096 | 245,760 | 0.02 | 9.89
16,384 | 1,146,880 | 0.12 | 9.51
65,536 | 5,242,880 | 0.32 | 16.43
262,144 | 23,592,960 | 2.25 | 10.50
1,048,576 | 104,857,600 | 21.80 | 4.81
4,194,304 | 461,373,440 | 113.83 | 4.05
16,777,216 | 2,013,265,920 | 468.63 | 4.30
(`N` gigaflops = `Nx109` operations per second)
Generated by gpuBench v1.7: 2017-02-19 15:03:10 | Back to summary