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
Rafael Castro developed a compatibility and performance enhancement for Mojo reductions in the modularml/mojo repository, focusing on optimizing small-axis tensor reductions on the GPU. He implemented a dedicated small_reduce_kernel using Mojo and CUDA, targeting cases where the reduction axis is smaller than a warp to improve efficiency for common workloads. His work included updating the reduction example to align with the latest Mojo compiler and ensuring it remained runnable with current toolchains. By adding a special case in the standard library for small tensor reductions, Rafael improved both the reliability and maintainability of Mojo’s reduction operations through low-level optimization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
273
Activity Months1
Your Network
198 people
Work 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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