pytorch/ao
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Machine LearningPerformance BenchmarkingPyTorch
Swift.Sun
PROFILE
Swift.sun
Jiwei Sun developed performance-focused features for PyTorch and Intel XPU workloads, contributing to the pytorch/ao and intel/torch-xpu-ops repositories. He implemented Intel XPU benchmarking support in PyTorch, updating memory profiling and synchronization to ensure accurate performance metrics and broader hardware compatibility. In intel/torch-xpu-ops, he built a SYCL-based linear integer 4 kernel for XPU, optimizing matrix multiplication with quantized weights to improve inference throughput and energy efficiency. His work leveraged C++, Python, and GPU programming expertise, demonstrating depth in performance benchmarking, quantization, and cross-hardware optimization, with a focus on robust feature delivery and code quality over a two-month period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
504
Activity Months2
Your Network
217 people
Work History
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for intel/torch-xpu-ops focused on performance-oriented feature delivery and code quality. Delivered a Linear Integer 4 Kernel for XPU with Quantized Weights, implemented via SYCL to improve matrix-multiplication throughput and bandwidth efficiency across diverse XPU hardware configurations. This work provides a foundation for faster quantized-model inference and reduced data movement, contributing to better latency and energy efficiency in production workloads. No critical bugs reported this month; feature development and stability were the primary focus.
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for intel/torch-xpu-ops focused on performance-oriented feature delivery and code quality. Delivered a Linear Integer 4 Kernel for XPU with Quantized Weights, implemented via SYCL to improve matrix-multiplication throughput and bandwidth efficiency across diverse XPU hardware configurations. This work provides a foundation for faster quantized-model inference and reduced data movement, contributing to better latency and energy efficiency in production workloads. No critical bugs reported this month; feature development and stability were the primary focus.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on pytorch/ao: Delivered Intel XPU Benchmarking Support, updated memory profiling/synchronization for XPU, and README documentation; committed as part of (#1259). Impact: broader hardware coverage, improved benchmarking accuracy, and clearer performance visibility for Intel XPU workloads.
November 2024
1 Commits • 1 Features
Nov 1, 2024Concise monthly summary for 2024-11 focused on pytorch/ao: Delivered Intel XPU Benchmarking Support, updated memory profiling/synchronization for XPU, and README documentation; committed as part of (#1259). Impact: broader hardware coverage, improved benchmarking accuracy, and clearer performance visibility for Intel XPU workloads.
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
GPU programmingMachine LearningPerformance BenchmarkingPyTorchSYCLmatrix multiplicationperformance optimizationquantization
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline
intel/torch-xpu-ops
Jan 2025 – Jan 2025
1 Month active
Languages Used
C++Python
Technical Skills
GPU programmingSYCLmatrix multiplicationperformance optimizationquantization