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Awesome GitHub RepositoriesInter-Process Communication Optimizations
Techniques for reducing latency by co-locating components and using shared memory instead of network calls.
Distinct from End-to-End Communication Channels: None of the candidates cover the architectural decision of using shared memory to eliminate network I/O for latency reduction.
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Awesome Inter-Process Communication Optimizations GitHub Repositories
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- bytedance/byteps
bytedance/byteps
3,721Vezi pe GitHubBytePS is a distributed deep neural network training framework and communication library designed to scale model training across multiple GPUs and compute nodes. It functions as a GPU cluster orchestrator and RDMA network optimizer, providing the necessary primitives to synchronize gradients and data across a server cluster. The project distinguishes itself through high-performance network optimizations, utilizing remote direct memory access and page-aligned memory to reduce latency. It employs topology-aware communication tuning and CPU core affinity management to maximize hardware throughpu
Uses shared-memory segments to accelerate data exchange between worker and server processes on the same machine.
Pythondeep-learningdistributed-trainingkeras
