首页|State-of-the-Art MPI Allreduce Implementations for Distributed Machine Learning: A Survey
State-of-the-Art MPI Allreduce Implementations for Distributed Machine Learning: A Survey
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NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from os f.io: "Efficient data communication is pivotal in distributed machine learning to mana ge the increased computational demands posed by large datasets and complex model s. "This survey explores the critical role of MPI Allreduce, a collective communica tion operation, in enhancing the scalability and performance of distributed mach ine learning. We examine traditional MPI libraries such as MPICH and Open MPI, which offer foundational support across diverse computing environments. Additiona lly, we delve into specialized implementations like NVIDIA's NCCL and Intel's on eCCL, designed to optimize performance on specific hardware platforms. Through a series of case studies, we demonstrate the impact of these optimized MPI Allred uce implementations on training times and model accuracy in real-world applicati ons, such as large-scale image classification and natural language processing.
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NSTL
