pytorch/pytorch
Mar 2026 – Mar 2026
1 Month active
Languages Used
C++Python
Technical Skills
C++C++ developmentDistributed systemsGPU ProgrammingPerformance ProfilingProfiling and debugging
Marvin Dsouza
PROFILE
Marvin Dsouza
Marvin Dz implemented cross-rank NCCL trace correlation in the pytorch/pytorch repository by introducing sequence number propagation for NCCL collective operations. Using C++ and Python, Marvin integrated the sequence number from ProcessGroupNCCL through ParamCommsDebugInfo into Kineto profiler traces, updating core data structures and macros to support this feature. This approach enabled all participating ranks to share a common identifier, improving trace matching and observability for distributed training. Marvin also added automated tests and updated Kineto submodules to ensure end-to-end support for GPU kernel event tracing, demonstrating a deep understanding of distributed systems and performance profiling workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
71
Activity Months1
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3455 peopleSame Organization
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Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 performance highlights: Implemented NCCL sequence number propagation to Kineto traces to enable cross-rank correlation, integrated end-to-end from ProcessGroupNCCL through ParamCommsDebugInfo to Kineto trace outputs, updated Kineto integration and associated data structures, added automated tests and submodule updates. This enhances observability for distributed training and lays groundwork for faster debugging and optimization.
3 Commits • 1 Features
Mar 1, 2026March 2026 performance highlights: Implemented NCCL sequence number propagation to Kineto traces to enable cross-rank correlation, integrated end-to-end from ProcessGroupNCCL through ParamCommsDebugInfo to Kineto trace outputs, updated Kineto integration and associated data structures, added automated tests and submodule updates. This enhances observability for distributed training and lays groundwork for faster debugging and optimization.
March 2026
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Quality Metrics
Correctness100.0%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++C++ developmentDistributed systemsGPU ProgrammingPerformance ProfilingProfiling and debuggingPython scripting
Repositories Contributed To
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