WebCal
Ctrl K

मार्गदर्शिका

Multi-machine, multi-GPU parallel processing

मार्गदर्शिकाएकीकरणसामान्य प्रश्नसहायता
मूल्यब्लॉग
त्वरित प्रारंभTop-Up and BillingAccelerating Access to Academic ResourcesQuick StartIntroductionMaintenance and TroubleshootingNetwork
प्लेटफ़ॉर्मJetBrain ProjectorTmpWebCal Scholars Program @2026Introduction to the Public Beta/Suqian Zone AAbout UsCopying Data Between InstancesAnalysis of Server Performance MetricsTidal Computing PowerLoad Balancing
घरेलू चिप्सUsing Huawei MindIEHuawei Ascend NPUMooreThread GPU
परिवेश सेटअपCUDA/cuDNNMinicondaPython3.XInstalling DependenciesOverviewImages
एंटरप्राइज़ सुविधाएँFlexible DeploymentElastic Deployment Release NotesBest Practices for Elastic DeploymentPerformance Metrics Monitoring
कंटेनर इंस्टेंसJupyterLabRemote SSH ConnectionSave the imageScaling ConfigurationMulti-machine, multi-GPU parallel processingDaemonOverviewChange the billing methodMigration Example (Same Region)Migration ExamplesRemote DesktopReset the system
GPU चुननाGPU SelectionPerformance Testing
डेटाUpload DataDownload DataPublic DataPublic Cloud Storage (Highly Recommended)Compression / DecompressionFile StorageLocal data diskOverview
सर्वोत्तम अभ्यासFileZillaGitGromacsHuggingFaceKataGoLinux BasicsMPIOpenCLPyCharm Remote DevelopmentR (RStudio) InstallationSSH TunnelTensorBoardRemote Development with VSCodeVisdomVulkanXShellOpen PortsWeChat MessagesPerformanceExpose multiple servicesMoney-Saving TipsComputation Precision IssuesSoftware Sources

कंटेनर इंस्टेंस

Multi-machine, multi-GPU parallel processing

30/07/202651607 व्यू

Given that GPUs other than the A100 and similar models lack IB network and NVLink hardware support, multi-machine parallel processing is less efficient than single-machine parallel processing; therefore, we no longer support enabling internal IP addresses for multi-machine, multi-card parallel processing

If your computing needs can be met with a single-machine, multi-GPU setup, we highly recommend this approach (multi-machine parallel computing incurs significant network overhead, and its parallel efficiency is far lower than that of a single-machine, multi-GPU setup).For multi-GPU on a single machine, simply rent multiple GPUs within the same instance. For instances that are already running, you can change the number of GPUs by shutting down the instance and then scaling up or down; see Scaling.

अगला लेखAbout Us
Compute Rental दस्तावेज़मार्गदर्शिका पर वापस जाएं