Keren Zhou
Keren Zhou
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GPU
Technical Review on PyTorch 2.0 and Triton
High-level overview of PyTorch 2.0 and Triton integration
Aug 7, 2023 10:03 PM — 10:03 PM
Virtual
Keren Zhou
Project
Slides
GPA
GPA is a performance advisor for NVIDIA GPUs that suggests potential code optimization opportunities at a hierarchy of levels, including individual lines, loops, and functions. GPA uses data flow analysis to approximately attribute measured instruction stalls to their root causes and uses information about a program’s structure and the GPU to match inefficiency patterns with suggestions for optimization. GPA estimates each optimization’s speedup based on a PC sampling-based performance model.
Code
HPCToolkit
Our tool provides a profile view and a trace view for GPU-accelerated applications. The profile view identifies where GPU APIs are invoked in CPU calling context, approximates calling context for GPU execution, and analyzes instruction mix for GPU kernels. The tool traces CPU and GPU activities for a large number of processes and threads with minimal overhead.
Code
DOC
Triton
Triton is a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment for expressing tensor math workloads that offers high flexibility, developer productivity and end to end performance.
Code
DOC
GVProf
We implemented GVProf, the first value profiler that locates value redundancy problems in applications running on GPU-based clusters. Our experiments show that GVProf incurs acceptable overhead and scales to large executions. GVProf provides useful insights to guide performance optimization. Under the guidance of GVProf, we optimized several HPC and machine learning workloads, obtaining speedups up to 1.93x.
Code
DOC
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 …
Aditya Desai
,
Keren Zhou
,
Anshumali Shrivastava
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Project
URL
Towards Agile Development of Efficient Deep Learning Operators (Hardware Insights)
Presented a talk about Triton and requested feedback from Intel engineers
Jun 29, 2023 10:56 PM — 10:56 PM
Virtual
Keren Zhou
Project
Slides
Towards Agile Development of Efficient Deep Learning Operators (Call for Contributions)
Presented a talk about Triton and called for contributions to improving the language
Jun 19, 2023 10:56 PM — 10:56 PM
Lake Tahoe, California
Keren Zhou
Project
Slides
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 …
Mao Lin
,
Keren Zhou
,
Pengfei Su
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Project
DOI
URL
Towards Agile Development of Efficient Deep Learning Operators (Pre-MLIR)
Presented triton programming language and its next step
Dec 2, 2022 10:03 PM — 10:03 PM
Virtual
Keren Zhou
Project
Slides
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