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Hardware-Aware Compression with Random Operation Access Specific Tile (ROAST) Hashing
Advancements in deep learning are often associated with increasing model sizes. Training and deploying large models require …
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DrGPUM: Guiding Memory Optimization for GPU-Accelerated Applications
GPUs are widely used in today’s computing platforms to accelerate applications in various domains. However, scarce GPU memory resources …
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Low Overhead and Context Sensitive Profiling of GPU-Accelerated Applications
As we near the end of Moore’s law scaling, the next-generation computing platforms are increasingly exploring heterogeneous …
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ValueExpert: Exploring Value Patterns in GPU-Accelerated Applications
General-purpose GPUs have become common in modern computing systems to accelerate applications in many domains, including machine …
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GPA: A GPU Performance Advisor Based on Instruction Sampling
Developing efficient GPU kernels can be difficult because of the complexity of GPU architectures and programming models. Existing …
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Measurement and Analysis of GPU-Accelerated OpenCL Computations on Intel GPUs
Graphics Processing Units (GPUs) have become a key technology for accelerating node performance in supercomputers, including the US …
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Outcomes of OpenMP Hackathon: OpenMP Application Experiences with the Offloading Model
This paper reports on experiences gained and practices adopted when using the latest features of OpenMP to port a variety of HPC …
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A Tool for Top-down Performance Analysis of GPU-Accelerated Applications
To support performance measurement and analysis of GPU-accelerated applications, we extended the HPCToolkit performance tools with …
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GVPROF: A Value Profiler for GPU-Based Clusters
GPGPUs are widely used in high-performance computing systems to accelerate scientific and machine learning workloads. Developing …
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Tools for Top-down Performance Analysis of GPU-Accelerated Applications
This paper describes extensions to Rice University’s HPCToolkit performance tools to support measurement and analysis of …
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