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 Architecture

https://arxiv.org/pdf/2203.04910

Source 2: Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated Large-Scale Data Analytics

https://arxiv.org/pdf/2502.09541

Source 3: GPU as Data Access Engines

https://files.futurememorystorage.com/proceedings/2024/20240808_NETC-301-1_Newburn.pdf

Source 4: Performance Analysis of Different IO Methods between GPU Memory and Storage

https://www.tkl.iis.u-tokyo.ac.jp/new/uploads/publication_file/file/1051/6C-03.pdf

Source 5: GDS cuFile API Reference - https://docs.nvidia.com/gpudirect-storage/api-reference-guide/index.html

Source 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 Datasets

https://www.nvidia.com/en-us/on-demand/session/gtc24-s62559/

Source 8: GMT: GPU Orchestrated Memory Tiering for the Big Data Era

https://dl.acm.org/doi/10.1145/3620666.3651353

Source 9: GPUDirect Storage

https://docs.nvidia.com/gpudirect-storage/

Source 10: GPUDirect Storage: A Direct Path Between Storage and GPU Memory

https://developer.nvidia.com/blog/gpudirect-storage/

Source 11: Introducing ROCm-DS: GPU-Accelerated Data Science for AMD Instinct™ GPUs

https://rocm.blogs.amd.com/software-tools-optimization/introducing-rocm-ds-revolutionizing-data-processing-with-amd-instinct-gpus/README.html

Source 12: NVIDIA and Storage Industry Leaders Unveil New Class of Enterprise Infrastructure for the Age of AI

https://nvidianews.nvidia.com/news/nvidia-and-storage-industry-leaders-unveil-new-class-of-enterprise-infrastructure-for-the-age-of-ai

Source 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/