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Manish Sudhir Sudumbrekar

PROFILE

Manish Sudhir Sudumbrekar

Developed backward-compatible mock device testing enhancements for the tenstorrent/tt-umd repository, focusing on robust support for legacy YAML cluster descriptors. The work involved implementing C++ logic to parse the legacy boardtype field and generate synthetic chip_unique_ids when absent, ensuring seamless compatibility with both older and modern descriptor formats. By integrating YAML parsing and backward compatibility handling, the solution enabled reliable testing across Wormhole N300 and N150 configurations. This feature also contributed to tt-metal’s mock device support with Fast Dispatch mode, aligning test coverage across platforms and reducing setup friction for legacy configurations, thereby improving the reliability and maintainability of test workflows.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

1Total
Bugs
0
Commits
1
Features
1
Lines of code
22
Activity Months1

Work History

December 2025

1 Commits • 1 Features

Dec 1, 2025

Month: 2025-12 — Delivered backward-compatible mock device testing enhancements in tenstorrent/tt-umd to enable robust testing with legacy YAML descriptors. Implemented parsing for the legacy boardtype field and synthetic chip_unique_ids when missing, ensuring compatibility with older configurations while preserving support for modern descriptor formats. Linked to tt-metal PR 34194 for mock device support with Fast Dispatch mode, with verification across Wormhole N300/N150 configurations.

Activity

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Quality Metrics

Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++

Technical Skills

Backward compatibility handlingC++ developmentYAML parsing

Repositories Contributed To

1 repo

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

tenstorrent/tt-umd

Dec 2025 Dec 2025
1 Month active

Languages Used

C++

Technical Skills

Backward compatibility handlingC++ developmentYAML parsing