OpenCL：MQL5中的内部实现测试 - 页 70

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tol64:

也许雷纳特可以看看能从这里面学到什么。新的规格很可能在MQL5中也会有更好的性能，不是吗？

至于C#/C++，如果需要，我们也可以甩掉它。最主要的是获得最大可能的产出。;)

目前，我让自己不要为Sharp重写CL优化器，希望新的MT4最终至少能提供1.1版本。 语言是一样的，编译器是一样的，没有任何主要障碍（我在MT4测试器中并不真的需要OpenCL支持，尽管如果它出现，我会继续这样做）。 如果它没有实现--我会向左思考。
 

在这样的机器上测试了这个主题中的一些脚本。

CPU-Z


CUDA-Z


对于每一个脚本，我都会提供一个链接到发布该脚本的帖子，以便其他人能够快速找到它，运行测试并在需要时比较结果。

测试1


测试2

2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Generation 013 (1280 passes, 140 ms) : MaxResult==116.05191; Average Result==106.7991
2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Generation 014 (1280 passes, 125 ms) : MaxResult==116.05191; Average Result==106.77599
2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Generation 015 (1280 passes, 125 ms) : MaxResult==116.05191; Average Result==106.37561
2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Generation 016 (1280 passes, 140 ms) : MaxResult==116.05191; Average Result==106.64193
2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Optimization finished. Best result == 116.05191 at 16 generation.
2013.11.29 14:29:13     ParallelOptimazer_00-02 (EURUSD,H1)     Total time of optimization == 2 sec 122 ms

测试3

规模=1000

CPU

GPU


测试4

2013.11.29 16:02:31     Tast_Mand_ (EURUSD,H1)  1872 msec

测试5

2013.11.29 16:39:50     ParallelTester_00-01 x (EURUSD,H1)       CLGetInfoInteger() returned 2
2013.11.29 16:39:51     ParallelTester_00-01 x (EURUSD,H1)       OpenCL init OK!
2013.11.29 16:39:51     ParallelTester_00-01 x (EURUSD,H1)       GPU time = 62 ms
2013.11.29 16:39:51     ParallelTester_00-01 x (EURUSD,H1)       Соunt indicators = 16; Count history bars = 144000; Count pass = 1280
2013.11.29 16:39:51     ParallelTester_00-01 x (EURUSD,H1)       Result on Gpu МахResult==1.34787 at 699 pass
2013.11.29 16:40:05     ParallelTester_00-01 x (EURUSD,H1)       CPU time = 14492 ms
2013.11.29 16:40:05     ParallelTester_00-01 x (EURUSD,H1)       Соunt indicators = 16; Count history bars = 144000; Count pass = 1280
2013.11.29 16:40:05     ParallelTester_00-01 x (EURUSD,H1)       Result on Cpu МахResult==1.34787 at 699 pass
2013.11.29 16:40:05     ParallelTester_00-01 x (EURUSD,H1)       CpuTime/GpuTime = 233.741935483871

测试6

2013.11.29 16:45:28     ParallelTester_00-01 x_cycle (EURUSD,H1) OpenCL init OK! Device number = 0
2013.11.29 16:45:28     ParallelTester_00-01 x_cycle (EURUSD,H1) GPU time = 577 ms
2013.11.29 16:45:28     ParallelTester_00-01 x_cycle (EURUSD,H1) Соunt indicators = 16; Count history bars = 144000; Count pass = 12800
2013.11.29 16:45:28     ParallelTester_00-01 x_cycle (EURUSD,H1) Result on Gpu МахResult==1.57161 at 7031 pass
2013.11.29 16:45:28     ParallelTester_00-01 x_cycle (EURUSD,H1) OpenCL init OK! Device number = 1
2013.11.29 16:45:29     ParallelTester_00-01 x_cycle (EURUSD,H1) GPU time = 546 ms
2013.11.29 16:45:29     ParallelTester_00-01 x_cycle (EURUSD,H1) Соunt indicators = 16; Count history bars = 144000; Count pass = 12800
2013.11.29 16:45:29     ParallelTester_00-01 x_cycle (EURUSD,H1) Result on Gpu МахResult==1.57161 at 7031 pass
2013.11.29 16:47:54     ParallelTester_00-01 x_cycle (EURUSD,H1) CPU time = 145144 ms
2013.11.29 16:47:54     ParallelTester_00-01 x_cycle (EURUSD,H1) Соunt indicators = 16; Count history bars = 144000; Count pass = 12800
2013.11.29 16:47:54     ParallelTester_00-01 x_cycle (EURUSD,H1) Result on Cpu МахResult==1.57161 at 7031 pass
2013.11.29 16:47:54     ParallelTester_00-01 x_cycle (EURUSD,H1) CpuTime/GpuTime = 265.8315018315018

测试7

2013.11.29 16:54:52     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     ========================================
2013.11.29 16:57:16     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     CPU time = 144691 ms
2013.11.29 16:57:16     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Соunt indicators = 16; Count history bars = 144000; Count pass = 12800
2013.11.29 16:57:16     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Result on Cpu МахResult==0.91969 at 4641 pass
2013.11.29 16:57:16     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     -------------------------
2013.11.29 16:57:16     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Device number = 0
2013.11.29 16:57:17     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     GPU time = 593 ms
2013.11.29 16:57:17     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     CpuTime/GpuTime = 243.9983136593592
2013.11.29 16:57:17     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Result on Gpu МахResult==0.91969 at 4641 pass
2013.11.29 16:57:17     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     ------------
2013.11.29 16:57:17     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Device number = 1
2013.11.29 16:57:18     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     GPU time = 546 ms
2013.11.29 16:57:18     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     CpuTime/GpuTime = 265.0018315018315
2013.11.29 16:57:18     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     Result on Gpu МахResult==0.91969 at 4641 pass
2013.11.29 16:57:18     ParallelTester_00-01 x_new_cycle (EURUSD,H1)     ------------

测试8

2013.11.29 17:08:08     vect_v2_all_devices (EURUSD,H1) =======================================
2013.11.29 17:08:08     vect_v2_all_devices (EURUSD,H1) OCL martices mul:         ROWS1 = 2000; COLSROWS = 2000; COLS2 = 2000
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) CPUTime = 64.085
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) ---------------
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) read = 4000000 elements
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) Device = 0: time = 0.251 sec.
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) CPUTime / GPUTotalTime = 255.319
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 1362,1715 ) = -5.34762192;    thirdCPU[ 1362,1715 ] = -5.34762192;    buf[ 1362,1715 ] = -5.34761715
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 365,218 ) = 1.04545093;    thirdCPU[ 365,218 ] = 1.04545093;    buf[ 365,218 ] = 1.04544997
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 1461,1678 ) = -0.26404253;    thirdCPU[ 1461,1678 ] = -0.26404253;    buf[ 1461,1678 ] = -0.26404306
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 1116,1765 ) = 0.61209172;    thirdCPU[ 1116,1765 ] = 0.61209172;    buf[ 1116,1765 ] = 0.61209279
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 256,499 ) = 2.50011539;    thirdCPU[ 256,499 ] = 2.50011539;    buf[ 256,499 ] = 2.50011611
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 528,1433 ) = 2.69000340;    thirdCPU[ 528,1433 ] = 2.69000340;    buf[ 528,1433 ] = 2.69000053
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 926,1280 ) = 4.74232054;    thirdCPU[ 926,1280 ] = 4.74232054;    buf[ 926,1280 ] = 4.74231577
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 361,1757 ) = 2.25322127;    thirdCPU[ 361,1757 ] = 2.25322127;    buf[ 361,1757 ] = 2.25322032
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 1441,400 ) = -1.65504980;    thirdCPU[ 1441,400 ] = -1.65504980;    buf[ 1441,400 ] = -1.65504801
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) sum( 1617,306 ) = -2.14686131;    thirdCPU[ 1617,306 ] = -2.14686131;    buf[ 1617,306 ] = -2.14686537
2013.11.29 17:09:12     vect_v2_all_devices (EURUSD,H1) ________________________
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) read = 4000000 elements
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) Device = 1: time = 0.734 sec.
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) CPUTime / GPUTotalTime = 87.309
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 370,1332 ) = 0.78463894;    thirdCPU[ 370,1332 ] = 0.78463894;    buf[ 370,1332 ] = 0.78463584
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 1346,515 ) = 4.13771629;    thirdCPU[ 1346,515 ] = 4.13771629;    buf[ 1346,515 ] = 4.13771629
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 632,631 ) = 0.53385985;    thirdCPU[ 632,631 ] = 0.53385985;    buf[ 632,631 ] = 0.53386015
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 930,102 ) = 6.17934942;    thirdCPU[ 930,102 ] = 6.17934942;    buf[ 930,102 ] = 6.17935467
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 507,167 ) = 2.76653004;    thirdCPU[ 507,167 ] = 2.76653004;    buf[ 507,167 ] = 2.76652718
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 1638,1623 ) = -3.40129304;    thirdCPU[ 1638,1623 ] = -3.40129304;    buf[ 1638,1623 ] = -3.40129256
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 208,649 ) = 8.09206963;    thirdCPU[ 208,649 ] = 8.09206963;    buf[ 208,649 ] = 8.09207344
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 298,741 ) = -0.59763604;    thirdCPU[ 298,741 ] = -0.59763604;    buf[ 298,741 ] = -0.59763324
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 1334,521 ) = -2.74508810;    thirdCPU[ 1334,521 ] = -2.74508810;    buf[ 1334,521 ] = -2.74508691
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) sum( 858,760 ) = -7.48025274;    thirdCPU[ 858,760 ] = -7.48025274;    buf[ 858,760 ] = -7.48025846
2013.11.29 17:09:13     vect_v2_all_devices (EURUSD,H1) ________________________


CPU-Z CPUID - System & hardware benchmark, monitoring, reporting
CPU-Z CPUID - System & hardware benchmark, monitoring, reporting
  • www.cpuid.com
CPU-Z is a freeware that gathers information on some of the main devices of your system.
 

我还试图测试MetaDriver的qpu_EMA-Rainbow 指标。


在CPU上，其结果有时会好上2倍。下面是结果。

2013.12.01 14:12:50     qpu_Future_EMA-Rainbow (EURUSD,M1)      Calculate 1000129 bars at CPU, time = 811 ms
2013.12.01 14:12:57     qpu_Future_EMA-Rainbow (EURUSD,M1)      OpenCL: GPU device 'GeForce GTX 650 Ti BOOST' selected
2013.12.01 14:12:58     qpu_Future_EMA-Rainbow (EURUSD,M1)      Calculate 1000129 bars at GPU (OpenCL), time = 1295 ms

//---

Volodya（MetaDriver），给我看看你的成果？

P.S. 我在内核代码中把gpuEMA 函数参数的类型从__global 改为__local。稍微快一点，但仍然比在CPU上慢。

2013.12.01 14:29:46     qpu_Future_EMA-Rainbow (EURUSD,M1)      Calculate 1000129 bars at CPU, time = 795 ms
2013.12.01 14:29:51     qpu_Future_EMA-Rainbow (EURUSD,M1)      OpenCL: GPU device 'GeForce GTX 650 Ti BOOST' selected
2013.12.01 14:29:52     qpu_Future_EMA-Rainbow (EURUSD,M1)      Calculate 1000129 bars at GPU (OpenCL), time = 1061 ms
附加的文件：
qpu_Future_EMA-Rainbow.mq5  14 kb
 
tol64:

我还试图测试MetaDriver的qpu_EMA-Rainbow 指标。

在CPU上，其结果有时会好上2倍。下面是结果。

Volodya（MetaDriver），给我看看你的成果？

P.S. 内核代码中的gpuEMA 函数参数从__global 改为__local。稍微快一点，但仍然比在CPU上慢。

我的结果是类似的。 这一点早就被讨论过了，而且是合乎逻辑的--任务太简单了，把内存转移到显卡上和从显卡上转移过来是没有效果的。 GPU的优势出现在更复杂的任务上。
 
MetaDriver:
我也有类似的结果。 这一点早就被讨论过了，而且很有道理--任务太简单了，把内存转移到显卡上和从显卡上转移到显卡上并不划算。 GPU的优势出现在更复杂的任务上。
我明白了，谢谢，我将用更复杂的任务进行实验。
 

一个将GPU加速用于交易（衍生品）的例子。

马克-乔希--以其关于金融数学，特别是关于衍生品和期权交易的书籍而闻名，曾在此报告过他的工作。

http://ssrn.com/abstract=2388415

他把他的OOP式工作翻译成了CUDA GPU。他在2010年开始做，然后休息了一段时间，从2011年到2014年夏天，他把它做到了工作版本0.3。他成功地实现了100倍的 加速度...137倍--而这是在一个连接的算法上，这很难。

这项工作使用了C++中的QuantLib库，他自己也承认，他不得不 按照 "OOP->>程序化方法 "的思路重新设计，以便使其在CUDA GPU上运行。

他写道。

"我已经在GPU上用LMM实现了IRD的蒙特卡洛定价，并对早期锻炼的特征进行了最小化。

你可以从kooderive.sourceforge.net获得C++和CUDA两种语言的代码。该文件在......

我在CUDA上使用的代码与之前在C++上使用的完全不同。从本质上讲，我把数据作为中心概念，并使用代码来对数据进行操作。这种风格是非常实用的。这确实花了很多功夫，因为我以前的C++实现是面向对象的。"

他的项目本身是开源的。

http://sourceforge.net/projects/kooderive/

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