modularml/mojo
Oct 2025 – Oct 2025
1 Month active
Languages Used
BazelMojo
Technical Skills
Build SystemsCUDA KernelsGPU ProgrammingLow-Level OptimizationPerformance BenchmarkingPerformance Optimization
Ruyman
PROFILE
Ruyman
Worked on the modularml/mojo repository to deliver compatibility and performance enhancements for Mojo reductions, focusing on optimizing small-axis tensor reductions on the GPU. Developed a dedicated small_reduce_kernel in Mojo to improve efficiency when the reduction axis is smaller than a warp, addressing common workload patterns. Updated the reduction example to ensure it remains runnable with the latest Mojo compiler and toolchains, enhancing maintainability. Leveraged skills in CUDA kernels, GPU programming, and low-level optimization to broaden support for small tensor reductions, including a special case in the standard library. Used Bazel and Mojo to implement and benchmark these performance improvements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
273
Activity Months1
Your Network
202 peopleWork History
October 2025
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Delivered compatibility and performance enhancements for Mojo reductions in modularml/mojo. Implemented the Mojo Reduction Feature to align with the latest Mojo compiler and optimize small-axis reductions on the GPU. Introduced a dedicated small_reduce_kernel for reductions where the axis is smaller than a warp, improving efficiency on common workloads. Ensured the reduction example remains runnable with current toolchains and added an stdlib special case for small tensor reductions to broaden support and reliability.
2 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Delivered compatibility and performance enhancements for Mojo reductions in modularml/mojo. Implemented the Mojo Reduction Feature to align with the latest Mojo compiler and optimize small-axis reductions on the GPU. Introduced a dedicated small_reduce_kernel for reductions where the axis is smaller than a warp, improving efficiency on common workloads. Ensured the reduction example remains runnable with current toolchains and added an stdlib special case for small tensor reductions to broaden support and reliability.
October 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BazelMojo
Technical Skills
Build SystemsCUDA KernelsGPU ProgrammingLow-Level OptimizationPerformance BenchmarkingPerformance OptimizationTensor Operations
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