<script type="application/ld+json">{"@context":"http://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://www.simcentric.com/tc/"},{"@type":"ListItem","position":2,"name":"什麼是 NVIDIA CUDA? GPU平行運算終極指南","item":"https://www.simcentric.com/tc/hong-kong-dedicated-server-tc/what-is-nvidia-cuda-a-guide-to-gpu-parallel-computing/"}]}</script> {"id":19206,"date":"2024-11-15T11:38:56","date_gmt":"2024-11-15T03:38:56","guid":{"rendered":"https:\/\/www.simcentric.com\/uncategorized-tc\/what-is-nvidia-cuda-a-guide-to-gpu-parallel-computing\/"},"modified":"2024-11-15T14:15:27","modified_gmt":"2024-11-15T06:15:27","slug":"what-is-nvidia-cuda-a-guide-to-gpu-parallel-computing","status":"publish","type":"post","link":"https:\/\/www.simcentric.com\/tc\/hong-kong-dedicated-server-tc\/what-is-nvidia-cuda-a-guide-to-gpu-parallel-computing\/","title":{"rendered":"\u4ec0\u9ebc\u662f NVIDIA CUDA? GPU\u5e73\u884c\u904b\u7b97\u7d42\u6975\u6307\u5357"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>\u8a8d\u8b58CUDA: GPU\u904b\u7b97\u7684\u9769\u547d\u6027\u7a81\u7834<\/strong><\/h2>\n<p>NVIDIA CUDA\uff08\u7d71\u4e00\u8a08\u7b97\u88dd\u7f6e\u67b6\u69cb\uff09\u662f\u4e00\u500b\u9769\u547d\u6027\u7684\u5e73\u884c\u904b\u7b97\u5e73\u53f0\uff0c\u5fb9\u5e95\u6539\u8b8a\u4e86<a href=\"https:\/\/www.simcentric.com\/tc\/products\/dedicated-server-hk\/\" target=\"_blank\" rel=\"noopener\">\u9999\u6e2f\u8cc7\u6599\u4e2d\u5fc3<\/a>\u9ad8\u6548\u80fd\u904b\u7b97\u7684\u683c\u5c40\u3002\u96a8\u8457<a href=\"https:\/\/www.simcentric.com\/tc\/hong-kong-dedicated-server-tc\/nvidia-gpu-sparse-computing-for-hong-kong-data-centers\/\" target=\"_blank\" rel=\"noopener\">GPU\u904b\u7b97<\/a>\u7684\u4e0d\u65b7\u767c\u5c55\uff0c\u7406\u89e3CUDA\u5c0d\u65bc\u7ba1\u7406\u4f3a\u670d\u5668\u57fa\u790e\u8a2d\u65bd\u7684\u6280\u8853\u5c08\u696d\u4eba\u54e1\u4f86\u8aaa\u8b8a\u5f97\u81f3\u95dc\u91cd\u8981\u3002<\/p>\n<p> [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>CUDA\u67b6\u69cb\u6838\u5fc3\u6982\u5ff5<\/strong><\/h2>\n<p>CUDA\u7684\u6838\u5fc3\u5728\u65bc\u5be6\u73fe\u76f4\u63a5\u7684GPU\u7a0b\u5f0f\u8a2d\u8a08\uff0c\u5229\u7528\u6578\u5343\u500b\u6838\u5fc3\u9032\u884c\u5e73\u884c\u8655\u7406\u3002\u8207\u50b3\u7d71\u7684CPU\u67b6\u69cb\u4e0d\u540c\uff0cCUDA\u7684\u5e73\u884c\u8655\u7406\u7bc4\u5f0f\u5141\u8a31\u540c\u6642\u57f7\u884c\u591a\u500b\u4efb\u52d9\uff0c\u9019\u4f7f\u5176\u7279\u5225\u9069\u5408\u904b\u7b97\u5bc6\u96c6\u578b\u61c9\u7528\u3002<\/p>\n<p>  [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>\u6280\u8853\u6df1\u5ea6\u5256\u6790\uff1aCUDA\u5be6\u73fe<\/strong><\/h2>\n<p>\u8b93\u6211\u5011\u4f86\u770b\u4e00\u500b\u5be6\u969b\u7684CUDA\u5be6\u73fe\u7bc4\u4f8b\u3002\u4ee5\u4e0b\u662f\u4e00\u500b\u5411\u91cf\u52a0\u6cd5\u7684\u7c21\u55ae\u7bc4\u4f8b\uff1a<\/p>\n<pre><code>__global__ void vectorAdd(float* a, float* b, float* c, int n) {\r\n    int idx = blockIdx.x * blockDim.x + threadIdx.x;\r\n    if (idx < n) {\r\n        c[idx] = a[idx] + b[idx];\r\n    }\r\n}\r\n\r\nint main() {\r\n    int n = 1<<20; \/\/ 1M elements\r\n    size_t bytes = n * sizeof(float);\r\n    \r\n    \/\/ \u5206\u914d\u4e3b\u6a5f\u8a18\u61b6\u9ad4\r\n    float *h_a = (float*)malloc(bytes);\r\n    float *h_b = (float*)malloc(bytes);\r\n    float *h_c = (float*)malloc(bytes);\r\n    \r\n    \/\/ \u521d\u59cb\u5316\u9663\u5217\r\n    for(int i = 0; i < n; i++) {\r\n        h_a[i] = rand()\/(float)RAND_MAX;\r\n        h_b[i] = rand()\/(float)RAND_MAX;\r\n    }\r\n    \r\n    \/\/ \u5206\u914d\u88dd\u7f6e\u8a18\u61b6\u9ad4\r\n    float *d_a, *d_b, *d_c;\r\n    cudaMalloc(&#038;d_a, bytes);\r\n    cudaMalloc(&#038;d_b, bytes);\r\n    cudaMalloc(&#038;d_c, bytes);\r\n    \r\n    \/\/ \u5c07\u8cc7\u6599\u8907\u88fd\u5230\u88dd\u7f6e\r\n    cudaMemcpy(d_a, h_a, bytes, cudaMemcpyHostToDevice);\r\n    cudaMemcpy(d_b, h_b, bytes, cudaMemcpyHostToDevice);\r\n    \r\n    \/\/ \u555f\u52d5\u6838\u5fc3\r\n    int blockSize = 256;\r\n    int numBlocks = (n + blockSize - 1) \/ blockSize;\r\n    vectorAdd<<<numBlocks, blockSize>>>(d_a, d_b, d_c, n);\r\n    \r\n    \/\/ \u5c07\u7d50\u679c\u8907\u88fd\u56de\u4e3b\u6a5f\r\n    cudaMemcpy(h_c, d_c, bytes, cudaMemcpyDeviceToHost);\r\n    \r\n    \/\/ \u6e05\u7406\r\n    cudaFree(d_a);\r\n    cudaFree(d_b);\r\n    cudaFree(d_c);\r\n    free(h_a);\r\n    free(h_b);\r\n    free(h_c);\r\n    \r\n    return 0;\r\n}<\/code><\/pre>\n<p>  [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>CUDA\u5728\u9999\u6e2f\u8cc7\u6599\u4e2d\u5fc3\u7684\u61c9\u7528<\/strong><\/h2>\n<p>\u9999\u6e2f\u7684\u8cc7\u6599\u4e2d\u5fc3\u8d8a\u4f86\u8d8a\u591a\u5730\u5229\u7528CUDA\u9032\u884cAI\u8a13\u7df4\u3001\u52a0\u5bc6\u8ca8\u5e63\u6316\u7926\u548c\u79d1\u5b78\u904b\u7b97\u3002\u4f5c\u70ba\u91d1\u878d\u4e2d\u5fc3\uff0c\u8a72\u57ce\u5e02\u7279\u5225\u91cd\u8996GPU\u52a0\u901f\u5728\u9ad8\u983b\u4ea4\u6613\u548c\u5373\u6642\u8cc7\u6599\u5206\u6790\u4e2d\u7684\u61c9\u7528\u50f9\u503c\u3002<\/p>\n<h2><strong>\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u7684CUDA\u6548\u80fd\u6700\u4f73\u5316<\/strong><\/h2>\n<p>\u5728\u9999\u6e2f\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u90e8\u7f72CUDA\u61c9\u7528\u6642\uff0c\u9700\u8981\u8003\u616e\u4ee5\u4e0b\u95dc\u9375\u56e0\u7d20\uff1a<\/p>\n<ul>\n<li>\u8a18\u61b6\u9ad4\u983b\u5bec\u6700\u4f73\u5316<\/li>\n<li>\u9ad8\u5bc6\u5ea6\u4f3a\u670d\u5668\u6a5f\u67b6\u7684\u6563\u71b1\u7ba1\u7406<\/li>\n<li>\u96fb\u529b\u6d88\u8017\u5e73\u8861<\/li>\n<li>\u5206\u6563\u5f0f\u904b\u7b97\u7684\u7db2\u8def\u5ef6\u9072\u6700\u5c0f\u5316<\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>\u5be6\u73fe\u6700\u5927CUDA\u6548\u80fd\u7684\u786c\u9ad4\u914d\u7f6e<\/strong><\/h2>\n<p>\u5728\u9999\u6e2f\u4f3a\u670d\u5668\u8a17\u7ba1\u8a2d\u65bd\u4e2d\u5be6\u73fe\u6700\u4f73CUDA\u6548\u80fd\u9700\u8981\u8b39\u614e\u7684\u786c\u9ad4\u9078\u64c7\u3002\u4ee5\u4e0b\u662f\u8a73\u7d30\u7684\u914d\u7f6e\u6307\u5357\uff1a<\/p>\n<style>\n    table {\n        width: 100%;\n        border-collapse: collapse;\n        margin: 20px 0;\n    }<\/p>\n<p>    table, th, td {\n        border: 1px solid #ddd;\n    }<\/p>\n<p>    th, td {\n        padding: 12px;\n        text-align: left;\n    }<\/p>\n<p>    th {\n        background-color: #f5f5f5;\n    }<\/p>\n<p>    tr:nth-child(even) {\n        background-color: #fafafa;\n    }<\/p>\n<p>    tr:hover {\n        background-color: #f0f0f0;\n    }\n<\/style>\n<table>\n<tr>\n<th>\u7d44\u4ef6<\/th>\n<th>\u5efa\u8b70<\/th>\n<th>\u5c0d\u6548\u80fd\u7684\u5f71\u97ff<\/th>\n<\/tr>\n<tr>\n<td>GPU\u578b\u865f<\/td>\n<td>NVIDIA A100\/H100<\/td>\n<td>\u76f4\u63a5\u904b\u7b97\u80fd\u529b\uff0c\u8a18\u61b6\u9ad4\u983b\u5bec<\/td>\n<\/tr>\n<tr>\n<td>CPU<\/td>\n<td>AMD EPYC\/Intel Xeon<\/td>\n<td>\u4e3b\u6a5f\u64cd\u4f5c\uff0c\u8cc7\u6599\u6e96\u5099<\/td>\n<\/tr>\n<tr>\n<td>\u7cfb\u7d71\u8a18\u61b6\u9ad4<\/td>\n<td>256GB+ DDR4\/DDR5<\/td>\n<td>\u8cc7\u6599\u7de9\u885d\uff0c\u7cfb\u7d71\u56de\u61c9\u6027<\/td>\n<\/tr>\n<tr>\n<td>\u5132\u5b58<\/td>\n<td>NVMe SSD\u9663\u5217<\/td>\n<td>\u8cc7\u6599\u8f09\u5165\u901f\u5ea6\uff0c\u81e8\u6642\u5132\u5b58<\/td>\n<\/tr>\n<\/table>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>CUDA\u6548\u80fd\u57fa\u6e96\u6e2c\u8a66<\/strong><\/h2>\n<p>\u4ee5\u4e0b\u662f\u4f7f\u7528CUDA Events\u9032\u884c\u57fa\u6e96\u6e2c\u8a66\u7684\u5be6\u969b\u5be6\u73fe\uff1a<\/p>\n<pre><code>cudaEvent_t start, stop;\r\ncudaEventCreate(&start);\r\ncudaEventCreate(&stop);\r\n\r\n\/\/ \u958b\u59cb\u8a08\u6642\r\ncudaEventRecord(start);\r\n\r\n\/\/ \u5728\u6b64\u8655\u555f\u52d5\u60a8\u7684CUDA\u6838\u5fc3\r\nmyKernel<<<gridSize, blockSize>>>(params);\r\n\r\n\/\/ \u505c\u6b62\u8a08\u6642\r\ncudaEventRecord(stop);\r\ncudaEventSynchronize(stop);\r\n\r\nfloat milliseconds = 0;\r\ncudaEventElapsedTime(&milliseconds, start, stop);\r\nprintf(\"\u6838\u5fc3\u57f7\u884c\u6642\u9593: %f \u6beb\u79d2\\n\", milliseconds);\r\n\r\n\/\/ \u6e05\u7406\r\ncudaEventDestroy(start);\r\ncudaEventDestroy(stop);<\/code><\/pre>\n<p>  [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=&#8221;blog-detail-section&#8221;][vc_column][vc_column_text]<\/p>\n<h2><strong>\u5e38\u898bCUDA\u5be6\u65bd\u6311\u6230<\/strong><\/h2>\n<p>\u5728\u9999\u6e2f\u4f3a\u670d\u5668\u79df\u7528\u74b0\u5883\u4e2d\u90e8\u7f72CUDA\u61c9\u7528\u6642\uff0c\u958b\u767c\u4eba\u54e1\u7d93\u5e38\u9047\u5230\u9019\u4e9b\u6311\u6230\uff1a<\/p>\n<ul>\n<li>\u8a18\u61b6\u9ad4\u7ba1\u7406\u8907\u96dc\u6027<\/li>\n<li>\u91dd\u5c0d\u4e0d\u540cGPU\u67b6\u69cb\u7684\u6838\u5fc3\u6700\u4f73\u5316<\/li>\n<li>\u591aGPU\u9593\u7684\u8ca0\u8f09\u5e73\u8861<\/li>\n<li>\u8207\u73fe\u6709\u57fa\u790e\u8a2d\u65bd\u7684\u6574\u5408<\/li>\n<\/ul>\n<h2><strong>\u751f\u7522\u74b0\u5883\u4e2d\u7684CUDA\u6700\u4f73\u5be6\u8e10<\/strong><\/h2>\n<p>\u70ba\u5728\u9999\u6e2f\u8cc7\u6599\u4e2d\u5fc3\u6700\u5927\u5316CUDA\u6548\u80fd\uff0c\u5be6\u65bd\u9019\u4e9b\u7d93\u9a57\u8b49\u7684\u7b56\u7565\uff1a<\/p>\n<pre><code>\/\/ \u9ad8\u6548\u8a18\u61b6\u9ad4\u5408\u4f75\u7684\u7bc4\u4f8b\r\n__global__ void efficientKernel(float* data, int pitch, int width, int height) {\r\n    int tidx = blockIdx.x * blockDim.x + threadIdx.x;\r\n    int tidy = blockIdx.y * blockDim.y + threadIdx.y;\r\n    \r\n    if (tidx < width &#038;&#038; tidy < height) {\r\n        \/\/ \u5408\u4f75\u7684\u8a18\u61b6\u9ad4\u5b58\u53d6\u6a21\u5f0f\r\n        int offset = tidy * pitch + tidx;\r\n        data[offset] = performComputation(data[offset]);\r\n    }\r\n}<\/code><\/pre>\n<p>  [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=\"blog-detail-section\"][vc_column][vc_column_text]<\/p>\n<h2><strong>CUDA\u5728\u9999\u6e2f\u79d1\u6280\u9818\u57df\u7684\u672a\u4f86<\/strong><\/h2>\n<p>CUDA\u6280\u8853\u7684\u767c\u5c55\u6301\u7e8c\u5851\u9020\u8457\u9999\u6e2f\u7684\u4f3a\u670d\u5668\u79df\u7528\u7522\u696d\u3002\u65b0\u8208\u8da8\u52e2\u5305\u62ec\uff1a<\/p>\n<ul>\n<li>\u8207\u91cf\u5b50\u904b\u7b97\u6846\u67b6\u7684\u6574\u5408<\/li>\n<li>\u589e\u5f37\u5c0dAI\/ML\u5de5\u4f5c\u8ca0\u8f09\u7684\u652f\u63f4<\/li>\n<li>\u6539\u9032\u7684\u7bc0\u80fd\u6f14\u7b97\u6cd5<\/li>\n<li>\u5148\u9032\u7684\u8a18\u61b6\u9ad4\u7ba1\u7406\u6280\u8853<\/li>\n<\/ul>\n<p>  [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=\"blog-detail-section\"][vc_column][vc_column_text]<\/p>\n<h2><strong>\u7d50\u8ad6\uff1a\u6700\u5927\u5316CUDA\u6f5b\u529b<\/strong><\/h2>\n<p>CUDA\u4ecd\u7136\u662f\u9999\u6e2f\u8cc7\u6599\u4e2d\u5fc3\u9ad8\u6548\u80fd\u904b\u7b97\u7684\u57fa\u790e\u3002\u96a8\u8457GPU\u904b\u7b97\u7684\u767c\u5c55\uff0c\u7406\u89e3\u548c\u6709\u6548\u5be6\u65bdCUDA\u5c0d\u4f3a\u670d\u5668\u79df\u7528\u63d0\u4f9b\u5546\u548c\u6280\u8853\u5c08\u696d\u4eba\u54e1\u4f86\u8aaa\u8b8a\u5f97\u8d8a\u4f86\u8d8a\u91cd\u8981\u3002\u900f\u904e\u9069\u7576\u7684\u6700\u4f73\u5316\u548c\u5be6\u65bd\u7b56\u7565\uff0c\u7d44\u7e54\u53ef\u4ee5\u5145\u5206\u5229\u7528CUDA\u7684\u5e73\u884c\u8655\u7406\u80fd\u529b\u4f86\u63d0\u9ad8\u6548\u80fd\u548c\u6548\u7387\u3002<\/p>\n<p>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row el_class=&#8221;blog-detail-section&#8221;][vc_ [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/www.simcentric.com\/tc\/hong-kong-dedicated-server-tc\/what-is-nvidia-cuda-a-guide-to-gpu-parallel-computing\/\">Read More&#8230;<\/a><\/p>\n","protected":false},"author":3,"featured_media":19199,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[61],"tags":[4763,4764,4609,633,247],"class_list":["post-19206","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hong-kong-dedicated-server-tc","tag-nvidia-cuda-tc","tag-cuda-cores-tc","tag-gpu","tag-data-center-tc","tag-hong-kong-server-rental-tc"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>\u4ec0\u9ebc\u662f NVIDIA CUDA? 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