MLP-Offload accelerates LLM pre-training on memory-constrained GPUs by mixing local NVMe and remote PFS offloading with cache-aware subgroup reordering, achieving up to 2.5x faster iterations than DeepSpeed ZeRO-3.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
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In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
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MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall
MLP-Offload accelerates LLM pre-training on memory-constrained GPUs by mixing local NVMe and remote PFS offloading with cache-aware subgroup reordering, achieving up to 2.5x faster iterations than DeepSpeed ZeRO-3.