NVIDIA GDS, BAM Vs RocM solutions

This is a huge review of 13 different sources on advancements in GPU-accelerated computing, focusing on data access, memory management, and performance optimization for large datasets. Several sources highlight NVIDIA's initiatives like GPUDirect Storage and the AI Data Platform, which streamline data transfer directly between storage and GPUs, reducing CPU bottlenecks. Conversely, other documents analyze AMD's efforts with ROCm, acknowledging its rapid software stack improvements but also pointing out challenges like lack of comprehensive Python support and the need for increased R&D investment to compete with NVIDIA's established CUDA ecosystem. Concepts such as GPU-orchestrated memory tiering and novel I/O primitives are presented as solutions to overcome limitations in GPU memory capacity and PCIe bandwidth, enabling more efficient processing of extensive data analytics and AI workloads.Source 1: GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecturehttps://arxiv.org/pdf/2203.04910Source 2: Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analyticshttps://arxiv.org/pdf/2502.09541Source 3: GPU as Data Access Engineshttps://files.futurememorystorage.com/proceedings/2024/20240808_NETC-301-1_Newburn.pdfSource 4: Performance Analysis of Different IO Methods between GPU Memory and Storagehttps://www.tkl.iis.u-tokyo.ac.jp/new/uploads/publication_file/file/1051/6C-03.pdfSource 5: GDS cuFile API Reference - https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.htmlSource 6: AMD 2.0 – New Sense of Urgency | MI450X Chance to Beat Nvidia | Nvidia’s New Moat Rapid Improvements, Developers First Approach, Low AMD AI Software Engineer Pay, Python DSL, UALink Disaster, MI325x, MI355x, MI430X UL4, MI450X Architecture, IF64/IF128, Flexible IO, UALink, IFoE https://semianalysis.com/2025/04/23/amd-2-0-new-sense-of-urgency-mi450x-chance-to-beat-nvidia-nvidias-new-moat/Source 7: Accelerating and Securing GPU Accesses to Large Datasetshttps://www.nvidia.com/en-us/on-demand/session/gtc24-s62559/Source 8: GMT: GPU Orchestrated Memory Tiering for the Big Data Erahttps://dl.acm.org/doi/10.1145/3620666.3651353Source 9: GPUDirect Storagehttps://docs.nvidia.com/gpudirect-storage/Source 10: GPUDirect Storage: A Direct Path Between Storage and GPU Memoryhttps://developer.nvidia.com/blog/gpudirect-storage/Source 11: Introducing ROCm-DS: GPU-Accelerated Data Science for AMD Instinct™ GPUshttps://rocm.blogs.amd.com/software-tools-optimization/introducing-rocm-ds-revolutionizing-data-processing-with-amd-instinct-gpus/README.htmlSource 12: NVIDIA and Storage Industry Leaders Unveil New Class of Enterprise Infrastructure for the Age of AIhttps://nvidianews.nvidia.com/news/nvidia-and-storage-industry-leaders-unveil-new-class-of-enterprise-infrastructure-for-the-age-of-aiSource 13: Why is CUDA so much faster than ROCm?https://www.reddit.com/r/MachineLearning/comments/1fa8vq5/d_why_is_cuda_so_much_faster_than_rocm/

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