Limited DRAM bandwidth poses a critical bottleneck in GPU performance, necessitating a comprehensive scheduling policy to reduce cache miss rates, enhance DRAM bandwidth, and improve latency hiding for GPUs. The CTA-aware scheduling techniques presented address these challenges by optimizing resourc

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PIM technology aims to enhance performance by moving computation closer to memory, improving bandwidth, latency, and energy efficiency. Despite initial setbacks, new strategies focus on cost-effectiveness, programming models, and overcoming implementation challenges. A new direction proposes intuiti

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Addressing memory latency challenges in irregular GPGPU applications, this study explores techniques like warp-aware memory scheduling and GPU memory controller optimization to reduce DRAM latency divergence. The research delves into the impact of SIMD lanes, coalescers, and warp-aware scheduling on

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This framework, GPUPerf, focuses on identifying potential benefits in GPGPU applications through performance analysis, modeling, and user-friendly metrics. It addresses the challenges programmers face in optimizing GPGPU code, providing guidance on program analysis and performance modeling. The fram

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This report outlines the progress made in implementing a new accelerated computing platform as part of the EGI-Engage JRA2 project. The project aimed to support GPGPU integration in batch systems and disciplines like structural biology and molecular dynamics. Key performance indicators and work plan

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This research focuses on addressing memory divergence issues in GPGPUs by exploiting inter-warp heterogeneity. By prioritizing mostly-hit warps and deprioritizing mostly-miss warps through Memory Divergence Correction (MeDiC), significant performance and energy efficiency improvements were achieved

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