{"id":2638,"date":"2020-05-19T09:00:00","date_gmt":"2020-05-19T09:00:00","guid":{"rendered":"https:\/\/www.thegioimaychu.vn\/solution\/?p=2638"},"modified":"2020-05-19T09:06:52","modified_gmt":"2020-05-19T09:06:52","slug":"kien-truc-nvidia-ampere","status":"publish","type":"post","link":"https:\/\/thegioimaychu.vn\/solution\/infra\/ai\/kien-truc-nvidia-ampere-p2638\/","title":{"rendered":"Ki\u1ebfn tr\u00fac NVIDIA Ampere"},"content":{"rendered":"

Th\u00e0nh ph\u1ea7n c\u1ed1t l\u00f5i c\u1ee7a AI v\u00e0 HPC trong trung t\u00e2m d\u1eef li\u1ec7u th\u1ebf h\u1ec7 m\u1edbi<\/h1>\n

C\u00e1c nh\u00e0 khoa h\u1ecdc, nh\u00e0 nghi\u00ean c\u1ee9u v\u00e0 k\u1ef9 s\u01b0 – nh\u1eefng “Da Vinci” v\u00e0 “Einstein” c\u1ee7a th\u1eddi \u0111\u1ea1i ch\u00fang ta – \u0111ang l\u00e0m vi\u1ec7c \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c th\u00e1ch th\u1ee9c quan tr\u1ecdng nh\u1ea5t c\u1ee7a th\u1ebf gi\u1edbi v\u1ec1 d\u1eef li\u1ec7u l\u1edbn, khoa h\u1ecdc, c\u00e1c ng\u00e0nh c\u00f4ng nghi\u1ec7p v\u1edbi AI v\u00e0\u00a0\u0111i\u1ec7n to\u00e1n hi\u1ec7u n\u0103ng cao (HPC)<\/a>.\u00a0Trong khi \u0111\u00f3, c\u00e1c doanh nghi\u1ec7p v\u00e0 th\u1eadm ch\u00ed to\u00e0n b\u1ed9 c\u00e1c ng\u00e0nh c\u00f4ng nghi\u1ec7p \u0111ang t\u00ecm c\u00e1ch khai th\u00e1c s\u1ee9c m\u1ea1nh c\u1ee7a AI \u0111\u1ec3 r\u00fat ra nh\u1eefng hi\u1ec3u bi\u1ebft m\u1edbi t\u1eeb c\u00e1c b\u1ed9 d\u1eef li\u1ec7u kh\u1ed5ng l\u1ed3, c\u1ea3 t\u1ea1i ch\u1ed7 v\u00e0 tr\u00ean \u0111\u00e1m m\u00e2y.\u00a0Ki\u1ebfn tr\u00fac NVIDIA Ampere, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf cho th\u1eddi \u0111\u1ea1i \u0111i\u1ec7n to\u00e1n linh ho\u1ea1t (elastic computing), mang \u0111\u1ebfn b\u01b0\u1edbc nh\u1ea3y v\u1ecdt k\u1ebf ti\u1ebfp v\u1edbi vi\u1ec7c cung c\u1ea5p kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c ch\u01b0a t\u1eebng th\u1ea5y \u1edf m\u1ecdi quy m\u00f4, cho ph\u00e9p c\u00e1c nh\u00e0 ph\u00e1t ki\u1ebfn n\u00e0y th\u1ef1c hi\u1ec7n c\u00f4ng vi\u1ec7c c\u1ee7a m\u00ecnh.<\/p>\n

\u0110\u1ed9t ph\u00e1 trong \u0111\u1ed5i m\u1edbi<\/h2>\n

\u0110\u01b0\u1ee3c ch\u1ebf t\u1ea1o v\u1edbi 54 t\u1ef7 transistors,\u00a0NVIDIA Ampere<\/a>\u00a0l\u00e0 con chip 7 nanomet (nm) l\u1edbn nh\u1ea5t t\u1eebng \u0111\u01b0\u1ee3c ch\u1ebf t\u1ea1o v\u00e0 c\u00f3 s\u00e1u c\u1ea3i ti\u1ebfn \u0111\u1ed9t ph\u00e1 quan tr\u1ecdng.<\/p>\n

\"L\u00f5i<\/p>\n

Tensor Core th\u1ebf h\u1ec7 th\u1ee9 3<\/h3>\n

\u0110\u01b0\u1ee3c gi\u1edbi thi\u1ec7u l\u1ea7n \u0111\u1ea7u ti\u00ean trong\u00a0ki\u1ebfn tr\u00fac\u00a0NVIDIA Volta\u2122, c\u00f4ng ngh\u1ec7 NVIDIA Tensor Core \u0111\u00e3 mang \u0111\u1ebfn s\u1ef1 t\u0103ng t\u1ed1c \u0111\u00e1ng kinh ng\u1ea1c cho AI, gi\u1ea3m th\u1eddi gian \u0111\u00e0o t\u1ea1o t\u1eeb v\u00e0i tu\u1ea7n xu\u1ed1ng h\u00e0ng gi\u1edd v\u00e0 cung c\u1ea5p kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c l\u1edbn \u0111\u1ec3 suy lu\u1eadn.\u00a0Ki\u1ebfn tr\u00fac NVIDIA Ampere \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng d\u1ef1a tr\u00ean nh\u1eefng c\u1ea3i ti\u1ebfn n\u00e0y b\u1eb1ng c\u00e1ch mang \u0111\u1ebfn m\u1ee9c \u0111\u1ed9 ch\u00ednh x\u00e1c m\u1edbi, Tensor Float (TF32) v\u00e0 Floating Point 64 (FP64), \u0111\u1ec3 t\u0103ng t\u1ed1c v\u00e0 \u0111\u01a1n gi\u1ea3n h\u00f3a vi\u1ec7c \u00e1p d\u1ee5ng AI, \u0111\u1ed3ng th\u1eddi m\u1edf r\u1ed9ng s\u1ee9c m\u1ea1nh c\u1ee7a Tensor Cores cho HPC.<\/p>\n

TF32 ho\u1ea1t \u0111\u1ed9ng gi\u1ed1ng nh\u01b0 FP32 trong khi cung c\u1ea5p t\u1ed1c \u0111\u1ed9 l\u00ean t\u1edbi 20 l\u1ea7n cho AI m\u00e0 kh\u00f4ng y\u00eau c\u1ea7u b\u1ea5t k\u1ef3 y\u00eau c\u1ea7u thay \u0111\u1ed5i code n\u00e0o.\u00a0S\u1eed d\u1ee5ng\u00a0NVIDIA Automatic Mixed Precision<\/a>, c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u c\u00f3 th\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c hi\u1ec7u su\u1ea5t g\u1ea5p 2 l\u1ea7n v\u1edbi automatic mixed precision v\u00e0 FP16 ch\u1ec9 th\u00eam m\u1ed9t v\u00e0i d\u00f2ng code.\u00a0V\u00e0 v\u1edbi s\u1ef1 h\u1ed7 tr\u1ee3 cho bfloat16, INT8 v\u00e0 INT4, c\u00e1c Tensor Core trong\u00a0GPU NVIDIA A100 Tensor Core<\/a>\u00a0t\u1ea1o ra m\u1ed9t h\u1ec7 th\u1ed1ng t\u0103ng t\u1ed1c c\u1ef1c k\u1ef3 linh ho\u1ea1t cho c\u1ea3 \u0111\u00e0o t\u1ea1o v\u00e0 suy lu\u1eadn AI.\u00a0Mang s\u1ee9c m\u1ea1nh c\u1ee7a Tensor Core cho HPC, A100 c\u0169ng cho ph\u00e9p c\u00e1c ph\u00e9p to\u00e1n ma tr\u1eadn \u1edf \u0111\u1ed9 ch\u00ednh x\u00e1c FP64 \u0111\u1ea7y \u0111\u1ee7, \u0111\u01b0\u1ee3c ch\u1ee9ng nh\u1eadn b\u1edfi IEEE.<\/p>\n

T\u00ccM HI\u1ec2U TH\u00caM V\u1ec0 TENSOR CORE<\/b><\/a><\/p>\n

Multi-Instance GPU (MIG)<\/h3>\n

M\u1ecdi \u1ee9ng d\u1ee5ng AI v\u00e0 HPC \u0111\u1ec1u c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c h\u01b0\u1edfng l\u1ee3i t\u1eeb vi\u1ec7c t\u0103ng t\u1ed1c, nh\u01b0ng kh\u00f4ng ph\u1ea3i m\u1ecdi \u1ee9ng d\u1ee5ng \u0111\u1ec1u c\u1ea7n hi\u1ec7u n\u0103ng c\u1ee7a m\u1ed9t h\u1ec7 th\u1ed1ng GPU A100 \u0111\u1ea7y \u0111\u1ee7.\u00a0V\u1edbi MIG, m\u1ed7i A100 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n chia th\u00e0nh b\u1ea3y GPU \u1ea3o (GPU instance), \u0111\u01b0\u1ee3c c\u00f4 l\u1eadp v\u00e0 b\u1ea3o m\u1eadt ho\u00e0n to\u00e0n \u1edf c\u1ea5p \u0111\u1ed9 ph\u1ea7n c\u1ee9ng v\u1edbi b\u1ed9 nh\u1edb b\u0103ng th\u00f4ng cao, b\u1ed9 nh\u1edb cache v\u00e0 core x\u1eed l\u00fd.\u00a0Gi\u1edd \u0111\u00e2y, c\u00e1c nh\u00e0 ph\u00e1t tri\u1ec3n c\u00f3 th\u1ec3 truy c\u1eadp kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c \u0111\u1ed9t ph\u00e1 cho t\u1ea5t c\u1ea3 c\u00e1c \u1ee9ng d\u1ee5ng c\u1ee7a h\u1ecd, c\u00e0 quy m\u00f4 l\u1edbn v\u00e0 nh\u1ecf v\u00e0 \u0111\u01b0\u1ee3c \u0111\u1ea3m b\u1ea3o v\u1ec1 m\u1eb7t QoS.\u00a0V\u00e0 c\u00e1c qu\u1ea3n tr\u1ecb vi\u00ean CNTT c\u00f3 th\u1ec3 cung c\u1ea5p kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c GPU \u0111\u00fang nhu c\u1ea7u \u0111\u1ec3 s\u1eed d\u1ee5ng t\u1ed1i \u01b0u v\u00e0 m\u1edf r\u1ed9ng quy\u1ec1n truy c\u1eadp cho m\u1ecdi ng\u01b0\u1eddi d\u00f9ng v\u00e0 \u1ee9ng d\u1ee5ng tr\u00ean c\u1ea3 m\u00f4i tr\u01b0\u1eddng \u1ea3o v\u00e0 v\u1eadt l\u00fd.<\/p>\n

\"GPU\u00a0\u00a0\"NVLink<\/p>\n

T\u00ccM HI\u1ec2U TH\u00caM V\u1ec0 MIG<\/b><\/a><\/p>\n

NVLink th\u1ebf h\u1ec7 th\u1ee9 ba<\/h3>\n

C\u00e1c \u1ee9ng d\u1ee5ng m\u1edf r\u1ed9ng tr\u00ean nhi\u1ec1u GPU \u0111\u00f2i h\u1ecfi d\u1eef li\u1ec7u di chuy\u1ec3n c\u1ef1c nhanh.\u00a0Th\u1ebf h\u1ec7 th\u1ee9 ba c\u1ee7a NVIDIA NVLink trong A100 t\u0103ng g\u1ea5p \u0111\u00f4i b\u0103ng th\u00f4ng tr\u1ef1c ti\u1ebfp t\u1eeb GPU \u0111\u1ebfn GPU l\u00ean \u0111\u1ebfn 600 GB m\u1ed7i gi\u00e2y (GB\/s), cao h\u01a1n g\u1ea7n 10 l\u1ea7n so v\u1edbi PCIe Gen4.\u00a0Khi \u0111\u01b0\u1ee3c gh\u00e9p n\u1ed1i v\u1edbi th\u1ebf h\u1ec7\u00a0NVIDIA NVSwitch m\u1edbi nh\u1ea5t<\/a>, t\u1ea5t c\u1ea3 c\u00e1c GPU trong m\u00e1y ch\u1ee7 c\u00f3 th\u1ec3 n\u00f3i chuy\u1ec7n v\u1edbi nhau \u1edf t\u1ed1c \u0111\u1ed9 NVLink \u0111\u1ea7y \u0111\u1ee7 \u0111\u1ec3 truy\u1ec1n d\u1eef li\u1ec7u c\u1ef1c nhanh.<\/p>\n

NVIDIA DGX\u2122\u00a0A100<\/a>\u00a0v\u00e0 c\u00e1c m\u00e1y ch\u1ee7 t\u1eeb c\u00e1c nh\u00e0 s\u1ea3n xu\u1ea5t m\u00e1y t\u00ednh h\u00e0ng \u0111\u1ea7u kh\u00e1c t\u1eadn d\u1ee5ng c\u00f4ng ngh\u1ec7 NVLink v\u00e0 NVSwitch th\u00f4ng qua c\u00e1c baseboard\u00a0NVIDIA\u00a0HGX\u2122\u00a0A100<\/a>\u00a0\u0111\u1ec3 mang l\u1ea1i kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng l\u1edbn h\u01a1n cho kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c HPC v\u00e0 AI.<\/p>\n

T\u00ccM HI\u1ec2U TH\u00caM V\u1ec0 NVLINK<\/b><\/a><\/p>\n

S\u1ef1 th\u01b0a th\u1edbt c\u00f3 c\u1ea5u tr\u00fac (Structural Sparsity)<\/h3>\n

C\u00e1c m\u1ea1ng AI hi\u1ec7n \u0111\u1ea1i c\u00f3 quy m\u00f4 r\u1ea5t l\u1edbn v\u00e0 ng\u00e0y c\u00e0ng m\u1edf r\u1ed9ng, v\u1edbi h\u00e0ng tri\u1ec7u v\u00e0 trong m\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p l\u00e0 h\u00e0ng t\u1ef7 tham s\u1ed1.\u00a0Kh\u00f4ng ph\u1ea3i t\u1ea5t c\u1ea3 c\u00e1c tham s\u1ed1 n\u00e0y \u0111\u1ec1u c\u1ea7n thi\u1ebft cho c\u00e1c ph\u00e9p d\u1ef1 \u0111o\u00e1n v\u00e0 suy lu\u1eadn ch\u00ednh x\u00e1c, v\u00e0 m\u1ed9t s\u1ed1 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c chuy\u1ec3n \u0111\u1ed5i th\u00e0nh zero \u0111\u1ec3 t\u1ea1o ra c\u00e1c m\u00f4 h\u00ecnh m\u00e0 “s\u1ef1 th\u01b0a th\u1edbt” m\u00e0 kh\u00f4ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn \u0111\u1ed9 ch\u00ednh x\u00e1c.\u00a0C\u00e1c Tensor Core trong A100 c\u00f3 th\u1ec3 cung c\u1ea5p hi\u1ec7u su\u1ea5t cao h\u01a1n g\u1ea5p 2 l\u1ea7n cho c\u00e1c m\u00f4 h\u00ecnh th\u01b0a th\u1edbt.\u00a0M\u1eb7c d\u00f9 t\u00ednh n\u0103ng th\u01b0a th\u1edbt d\u1ec5 d\u00e0ng mang l\u1ea1i l\u1ee3i \u00edch cho suy lu\u1eadn AI, n\u00f3 c\u0169ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh.<\/p>\n

<\/b>\"C\u1ea5u\u00a0\u00a0\"B\u1ed9<\/p>\n

T\u00ccM HI\u1ec2U TH\u00caM V\u1ec0 SPARSITY<\/b><\/a><\/p>\n

B\u1ed9 nh\u1edb th\u00f4ng minh v\u00e0 nhanh h\u01a1n<\/h3>\n

A100 \u0111ang mang \u0111\u1ebfn s\u1ed1 l\u01b0\u1ee3ng l\u1edbn kh\u1ea3 n\u0103ng x\u1eed l\u00fd cho c\u00e1c trung t\u00e2m d\u1eef li\u1ec7u.\u00a0\u0110\u1ec3 gi\u1eef cho c\u00e1c c\u00f4ng c\u1ee5 \u0111i\u1ec7n to\u00e1n n\u00e0y \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ea7y \u0111\u1ee7, n\u00f3 c\u00f3 b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb l\u00ean \u0111\u1ebfn 1,6 terabyte m\u1ed7i gi\u00e2y (TB\/s), t\u0103ng 67% so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc.\u00a0Ngo\u00e0i ra, A100 c\u00f3 b\u1ed9 nh\u1edb tr\u00ean chip nhi\u1ec1u h\u01a1n \u0111\u00e1ng k\u1ec3, bao g\u1ed3m b\u1ed9 nh\u1edb cache level 2 40 MB, l\u1edbn h\u01a1n 7 l\u1ea7n so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc, \u0111\u1ec3 t\u1ed1i \u0111a h\u00f3a hi\u1ec7u su\u1ea5t t\u00ednh to\u00e1n.<\/p>\n

T\u0103ng t\u1ed1c t\u1eadp trung \u1edf ngo\u00e0i r\u00eca (Edge)<\/h3>\n

S\u1ef1 k\u1ebft h\u1ee3p gi\u1eefa ki\u1ebfn \u200b\u200btr\u00fac NVIDIA Ampere v\u00e0 ConnectX-6 Dx SmartNIC c\u1ee7a NVIDIA Mellanox trong\u00a0NVIDIA EGX\u2122\u00a0A100<\/a>\u00a0mang \u0111\u1ebfn kh\u1ea3 n\u0103ng t\u00ednh to\u00e1n v\u00e0 t\u0103ng t\u1ed1c m\u1ea1ng ch\u01b0a t\u1eebng c\u00f3 \u0111\u1ec3 x\u1eed l\u00fd l\u01b0\u1ee3ng d\u1eef li\u1ec7u kh\u1ed5ng l\u1ed3 \u0111\u01b0\u1ee3c t\u1ea1o ra \u1edf r\u00eca m\u1ea1ng.\u00a0Mellanox SmartNIC bao g\u1ed3m kh\u1ea3 n\u0103ng gi\u1ea3m t\u1ea3i b\u1ea3o m\u1eadt v\u1edbi kh\u1ea3 n\u0103ng decrypt \u1edf t\u1ed1c \u0111\u1ed9 \u0111\u01b0\u1eddng truy\u1ec1n l\u00ean t\u1edbi 200 gigabit m\u1ed7i gi\u00e2y (Gb\/s) v\u00e0 c\u00f4ng ngh\u1ec7 GPUDirect\u2122 cho ph\u00e9p chuy\u1ec3n t\u1ea3i video frames tr\u1ef1c ti\u1ebfp v\u00e0o b\u1ed9 nh\u1edb GPU \u0111\u1ec3 x\u1eed l\u00fd AI.\u00a0V\u1edbi EGX A100, c\u00e1c doanh nghi\u1ec7p c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c vi\u1ec7c tri\u1ec3n khai AI \u1edf r\u00eca an to\u00e0n v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n r\u1ea5t nhi\u1ec1u.<\/p>\n

<\/b>\"Gia<\/p>\n

T\u00ccM HI\u1ec2U TH\u00caM V\u1ec0 NVIDIA EGX A100<\/b><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Th\u00e0nh ph\u1ea7n c\u1ed1t l\u00f5i c\u1ee7a AI v\u00e0 HPC trong trung t\u00e2m d\u1eef li\u1ec7u th\u1ebf h\u1ec7 m\u1edbi C\u00e1c nh\u00e0 khoa h\u1ecdc, nh\u00e0 nghi\u00ean c\u1ee9u v\u00e0 k\u1ef9 s\u01b0 – nh\u1eefng “Da Vinci” v\u00e0 “Einstein” c\u1ee7a th\u1eddi \u0111\u1ea1i ch\u00fang ta – \u0111ang l\u00e0m vi\u1ec7c \u0111\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c th\u00e1ch th\u1ee9c quan tr\u1ecdng nh\u1ea5t c\u1ee7a th\u1ebf gi\u1edbi v\u1ec1 d\u1eef<\/p>\n","protected":false},"author":1,"featured_media":2650,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,59],"tags":[27,118,22],"class_list":["post-2638","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-nvidia","tag-ai","tag-dgx","tag-nvidia"],"yoast_head":"\nKi\u1ebfn tr\u00fac NVIDIA Ampere - Gi\u1ea3i ph\u00e1p h\u1ea1 t\u1ea7ng CNTT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/thegioimaychu.vn\/solution\/infra\/ai\/kien-truc-nvidia-ampere-p2638\/\" \/>\n<meta property=\"og:locale\" content=\"vi_VN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ki\u1ebfn tr\u00fac NVIDIA Ampere - 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