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Jerry Mannil

PROFILE

Jerry Mannil

Worked on performance and reliability improvements in the PyTorch repository, focusing on the ROCm/MI300X path. Delivered targeted kernel and runtime optimizations to increase throughput for elementwise operations, using C++ and CUDA to implement non-vectorized loop unrolling, vectorized execution enhancements, and non-temporal loads. Addressed a critical reduction performance regression for NHWC 3D tensors by adjusting CUDA reduction configurations for non-contiguous ChannelsLast layouts. Improved GPU utilization by updating maxpool kernel launch configurations, optimizing block strides and thread limits. The work demonstrated depth in GPU programming, parallel computing, and performance optimization, resulting in measurable gains for PyTorch’s MI300X support.

Overall Statistics

Feature vs Bugs

67%Features

Repository Contributions

5Total
Bugs
1
Commits
5
Features
2
Lines of code
239
Activity Months1

Your Network

2489 people

Work History

May 2025

5 Commits • 2 Features

May 1, 2025

Concise monthly summary for 2025-05 focusing on performance and reliability improvements in the PyTorch ROCm/MI300X path. Delivered targeted kernel and runtime optimizations to boost throughput for elementwise ops, fixed a critical reduction performance regression for NHWC 3D tensors, and improved maxpool kernel launch configuration to enhance GPU utilization.

Activity

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Quality Metrics

Correctness100.0%
Maintainability80.0%
Architecture92.0%
Performance100.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++

Technical Skills

CUDACUDA programmingGPU ProgrammingGPU optimizationGPU programmingParallel computingPerformance OptimizationPerformance optimizationTensor Operations

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

pytorch/pytorch

May 2025 May 2025
1 Month active

Languages Used

C++

Technical Skills

CUDACUDA programmingGPU ProgrammingGPU optimizationGPU programmingParallel computing