High-Performance Big Data :: Home
Overview Welcome to the High-Performance Big Data project created by the Network-Based Computing Laboratory of The Ohio State University. The HiBD packages are being used by more than 370 organizations worldwide in 39 countries (Current Users) to accelerate Big Data applications. As of Nov '24, more than 49,750 downloads have taken place from this project's site. The HiBD project contains the foll
Slurmã§pytorch distributed trainingã‚’ã™ã‚‹ - Qiita
Introduction 環境 slurm 18.08 pytorch 1.3 What is Slurm? Slurmã¯ã€ã‚¹ãƒ‘コンやコンピュータクラスタãªã©ã§ä¸»ã«ç§‘å¦æŠ€è¡“計算用途ã§ç”¨ã„られるジョブスケジューラã®ä¸€ç¨®ã€‚SGE, Torque, LSFãªã©ã‚’使ã£ãŸã“ã¨ãŒã‚ã‚Œã°åŒæ§˜ã®ã‚‚ã®ã¨æ€ã£ã¦ã‚‚らã£ã¦ã„ã„。 ç§ã¯éŽåŽ»ã€SGEã¨LSFã¯ä½¿ã£ãŸã“ã¨ãŒã‚ã‚‹ãŒã€ç°¡å˜ã«Slurmã®ã„ã„ã¨ã“ã‚ã‚’ã‚ã’る㨠srunãŒä¾¿åˆ©ï¼ˆsubmit用ã®scriptを作らãªãã¦ã‚‚ã€ã‚¤ãƒ³ã‚¿ãƒ©ã‚¯ãƒ†ã‚£ãƒ–ã«ã‚³ãƒžãƒ³ãƒ‰ã‚’実行ã§ãる） GPUã®ãƒªã‚½ãƒ¼ã‚¹ç®¡ç†ãŒã§ãる（GPUを使用ã™ã‚‹ãƒ—ãƒã‚°ãƒ©ãƒ ã§æŽ’ä»–çš„ã«Deviceを確ä¿ã§ãる） 複数ノード・複数プãƒã‚»ã‚¹ã§ã®ä¸¦åˆ—実行ã®ã‚µãƒãƒ¼ãƒˆãŒå……実ã—ã¦ã„る。 今回ã®è©±ã¯ï¼“ã¤ç›®ã®ç‰¹å¾´ã«ã¤ã„ã¦ã€‚ What is PyTorch? FacebookãŒé–‹ç™ºã—ãŸDeep learningã®ãƒ•ãƒ¬ãƒ¼ãƒ ワーク。 ãªãœS
MVAPICH :: gdr Userguide
MVAPICH: MPI over InfiniBand, Omni-Path, Ethernet/iWARP, RoCE, and Slingshot Network-Based Computing Laboratory MVAPICH2-GDR 2.3.7 binary release is based on MVAPICH2 2.3.7 and incorporates designs that take advantage of GPUDirect RDMA technology enabling direct P2P communication between NVIDIA GPUs and Mellanox InfiniBand adapters. MVAPICH2-GDR 2.3.7 also adds support for AMD GPUs via Radeon Open
Infinibandã®æ€§èƒ½ãƒ™ãƒ³ãƒãƒžãƒ¼ã‚¯ï½žGPU Direct™を実行ã—ã¦ã¿ã‚‹ï½žï½œGPUãªã‚‰NTTPC｜NVIDIAエリートパートナー
今回ã¯ã€Infinibandã®é€šä¿¡ã«ãŠã‘るベンãƒãƒžãƒ¼ã‚¯æ–¹æ³•ã‚’紹介ã—ã¾ã™ã€‚ 特ã«ã€Infiniband経由ã§GPU Direct™を実行ã—ãŸå ´åˆã®ãƒ‘フォーマンスã«ã¤ã„ã¦èª¿æŸ»ã—ã¾ã™ï¼ 内容ãŒã¡ã‚‡ã£ã¨ãƒžãƒ‹ã‚¢ãƒƒã‚¯ã‹ã‚‚知れã¾ã›ã‚“ãŒã€ã‚¤ãƒ¡ãƒ¼ã‚¸ã ã‘ã§ã‚‚掴んã§ã‚‚らãˆã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ æ§‹æˆ ã‚µãƒ¼ãƒãƒ¼ ç体　：Supermicro® SYS-4028GR-TRT2 GPU　：NVIDIA® Tesla® V100 16GB Infiniband HCA ( Host Channel Adapter )　：Mellanox MCX353A-FCBT ( Connect X-3 VPI ) OS / ソフトウェア OS　：Ubuntu 16.04.4 LTS ベンãƒãƒžãƒ¼ã‚¯ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã€€ï¼šOSU Micro-Benchmarks 5.4.3 MPI ( 並列コンピューティング実行環境 )　：OpenMPI 2.1.
FAQ: Tuning the run-time characteristics of MPI TCP communications
This FAQ is for Open MPI v4.x and earlier. If you are looking for documentation for Open MPI v5.x and later, please visit docs.open-mpi.org. Table of contents: How do I specify to use the IP network for MPI messages? But wait — I'm using a high-speed network. Do I have to disable the TCP BTL? How do I know what MCA parameters are available for tuning MPI performance? Does Open MPI use the IP loopb
FAQ: Tuning the run-time characteristics of MPI InfiniBand, RoCE, and iWARP communications
FAQ: Tuning the run-time characteristics of MPI InfiniBand, RoCE, and iWARP communications
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https://mdx.jp/wp-content/uploads/2022/06/HPC183-mdx-hanawa.pdf
https://events.it4i.cz/event/123/attachments/417/1578/Technical%20features%20and%20the%20use%20of%20Karolina%20GPU%20accelerated%20partition.pdf
PyTorch Distributed with MPI
Dask-MPI with GPUs — Dask-MPI 2022.4.0+68.g551707e.dirty documentation
GitHub - dask/dask-mpi: Deploy Dask using MPI4Py
Efficient MPI-based Communication for GPU-Accelerated Dask Applications
volcano/example/integrations/mpi/mpi-example.yaml at master · volcano-sh/volcano
Permalinks
Benchmark Tests - NVIDIA GPUDirect RDMA - NVIDIA Networking Docs
GitHub - kubeflow/mpi-operator: Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
Production Multi-node Jobs with Gang Scheduling, K8s, GPUs... Madhukar Korupolu & Sanjay Chatterjee
https://slurm.schedmd.com/SLUG19/NVIDIA_Containers.pdf
スーパーコンピューティング - 高速ã«è¨ˆç®—ã™ã‚‹æŠ€è¡“ -
https://ipsj.ixsq.nii.ac.jp/ej/index.php?action=pages_view_main&active_action=repository_action_common_download&item_id=174468&item_no=1&attribute_id=1&file_no=1&page_id=13&block_id=8
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