modular/modular
Mar 2025 – Oct 2025
8 Months active
Languages Used
MojoPythonBazelStarlarkMarkdownYAML
Technical Skills
BenchmarkingCUDAClickCommand-Line InterfaceData VisualizationGPU Computing
Davood Mohajerani
PROFILE
Davood Mohajerani
Davood engineered advanced benchmarking and kernel tuning infrastructure for the modular/modular repository, focusing on GPU-accelerated performance and robust workflow automation. He designed and implemented features such as the Queryable Dispatch Table for SM90 matmul tuning, multi-GPU benchmarking support, and YAML-based configuration merging, leveraging Python, Mojo, and Bazel. His work included optimizing kernel dispatch logic, enhancing CLI usability, and modernizing build and CI pipelines to ensure reproducible, scalable results. By integrating parallel execution, resource-aware scheduling, and comprehensive testing, Davood delivered reliable, maintainable systems that improved benchmarking fidelity, accelerated tuning cycles, and enabled data-driven performance analysis across diverse hardware environments.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
81Total
Bugs
6
Commits
81
Features
30
Lines of code
20,723
Activity Months8
Your Network
82 people
Work History
October 2025
11 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary for modular/modular focused on advancing multi-GPU performance, benchmark tooling, and reliability. The team delivered substantial kernel-level optimizations, expanded multi-GPU support for benchmarking, enhanced YAML-based configuration merge capabilities, and stabilized common tooling paths to improve reproducibility and developer productivity.
11 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary for modular/modular focused on advancing multi-GPU performance, benchmark tooling, and reliability. The team delivered substantial kernel-level optimizations, expanded multi-GPU support for benchmarking, enhanced YAML-based configuration merge capabilities, and stabilized common tooling paths to improve reproducibility and developer productivity.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 20252025-09 Monthly Summary — modular/modular: Delivered notable KBench CLI enhancements for controlled benchmarking and advanced partitioning, and completed Gemma-27b SM90 tuning optimizations with a new tuning-list framework and matmul-dispatch integration. Also fixed a missing-values edge case in the Gemma SM90 dispatch path. These efforts deliver more reliable, repeatable benchmarks and hardware-aware performance improvements.
September 2025
5 Commits • 2 Features
Sep 1, 20252025-09 Monthly Summary — modular/modular: Delivered notable KBench CLI enhancements for controlled benchmarking and advanced partitioning, and completed Gemma-27b SM90 tuning optimizations with a new tuning-list framework and matmul-dispatch integration. Also fixed a missing-values edge case in the Gemma SM90 dispatch path. These efforts deliver more reliable, repeatable benchmarks and hardware-aware performance improvements.
August 2025
18 Commits • 5 Features
Aug 1, 2025August 2025 (2025-08) performance month for modular/modular focused on accelerating kernel tuning, robust benchmarking, and codebase reliability. Delivered the Queryable Dispatch Table (QDT) framework for SM90 FP8/FP16/FP32 matmul with prototype configurations, table structures, and shape-aware support for llama variants, enabling finer-grained tuning and dispatch control. Extended QDT coverage across multiple shapes (llama_405b_fp8, llama3.3.70b, Internvl shapes) and added M parameter support (M = 256, 1024, 8192) with new tuning constructors for split-k kernels. Benchmarking tooling improvements include GPU initialization for bench_allreduce (init_on_gpu) reducing per-parameter benchmarking time, robust handling of results (empty/NA entries), and improved pivot detection for comparing baselines vs tuned results. Benchmark configuration stabilization reduced noise through GPU count fixes (bench_allreduce), removal/disablement of noisy shapes (bench_matmul.yaml, bench_normalization), and general reliability improvements. Codebase reorganization and documentation updates for GPU communication kernels, plus CI pipeline dependency fixes to ensure consistent benchmarking across environments. Overall, achieved substantial performance improvements over main across multiple shapes, with faster iteration cycles and more reliable measurements, contributing to measurable business value through faster tuning cycles and more robust performance guarantees.
18 Commits • 5 Features
Aug 1, 2025August 2025 (2025-08) performance month for modular/modular focused on accelerating kernel tuning, robust benchmarking, and codebase reliability. Delivered the Queryable Dispatch Table (QDT) framework for SM90 FP8/FP16/FP32 matmul with prototype configurations, table structures, and shape-aware support for llama variants, enabling finer-grained tuning and dispatch control. Extended QDT coverage across multiple shapes (llama_405b_fp8, llama3.3.70b, Internvl shapes) and added M parameter support (M = 256, 1024, 8192) with new tuning constructors for split-k kernels. Benchmarking tooling improvements include GPU initialization for bench_allreduce (init_on_gpu) reducing per-parameter benchmarking time, robust handling of results (empty/NA entries), and improved pivot detection for comparing baselines vs tuned results. Benchmark configuration stabilization reduced noise through GPU count fixes (bench_allreduce), removal/disablement of noisy shapes (bench_matmul.yaml, bench_normalization), and general reliability improvements. Codebase reorganization and documentation updates for GPU communication kernels, plus CI pipeline dependency fixes to ensure consistent benchmarking across environments. Overall, achieved substantial performance improvements over main across multiple shapes, with faster iteration cycles and more reliable measurements, contributing to measurable business value through faster tuning cycles and more robust performance guarantees.
August 2025
July 2025
16 Commits • 4 Features
Jul 1, 2025July 2025 monthly summary for modular/modular focusing on GPU-accelerated benchmarking enhancements, scheduling and reporting improvements, and robust validation. Key outcomes include faster benchmark iterations, reduced build/compile overhead, richer and safer output formats, expanded codegen and YAML consolidation, and improved reliability through stronger process control and unit tests.
July 2025
16 Commits • 4 Features
Jul 1, 2025July 2025 monthly summary for modular/modular focusing on GPU-accelerated benchmarking enhancements, scheduling and reporting improvements, and robust validation. Key outcomes include faster benchmark iterations, reduced build/compile overhead, richer and safer output formats, expanded codegen and YAML consolidation, and improved reliability through stronger process control and unit tests.
June 2025
4 Commits • 2 Features
Jun 1, 2025June 2025: Delivered performance and benchmarking enhancements in modular/modular with a focus on accuracy, speed, and maintainability. Implemented Kprofile Performance Reporting Enhancements to expose a speedup metric and stabilize ratio calculations; modernized benchmarking tooling with modular dependency management, adding a requirements.txt and relocating utilities to autotune/utils.py for easier reuse; fixed a ParamSpace ordering bug to ensure consistent kplot comparisons across runs. These changes improve decision-making with more reliable performance data, reduce maintenance burden, and enable faster benchmarking cycles.
4 Commits • 2 Features
Jun 1, 2025June 2025: Delivered performance and benchmarking enhancements in modular/modular with a focus on accuracy, speed, and maintainability. Implemented Kprofile Performance Reporting Enhancements to expose a speedup metric and stabilize ratio calculations; modernized benchmarking tooling with modular dependency management, adding a requirements.txt and relocating utilities to autotune/utils.py for easier reuse; fixed a ParamSpace ordering bug to ensure consistent kplot comparisons across runs. These changes improve decision-making with more reliable performance data, reduce maintenance burden, and enable faster benchmarking cycles.
June 2025
May 2025
14 Commits • 6 Features
May 1, 2025May 2025 monthly summary for modular/modular: Delivered performance-oriented tooling and benchmarking capabilities that accelerate builds, improve benchmarking fidelity, and enhance data visualization. Key features include KBench CLI and Build/Performance Enhancements with parallel, CPU-aware builds; KBench Baseline Benchmarking with Empty Parameters for baseline comparisons; KPlot Plotting Tools Restored and Enhanced with Python-based plotting and profiling; Autotune Benchmarks Bazel Build Support with CI alignment; KProfile Enhancements and Differencing with dataclass refactor, pivots, and diffing; Bench Memcpy Config Serialization Enable Writable; and KBench BuildItem Robustness fixes to initialization. These contributions improve build speed, accuracy of performance measurements, and reliability of the benchmarking workflow, delivering clear business value in faster release cycles and better data-driven decisions.
May 2025
14 Commits • 6 Features
May 1, 2025May 2025 monthly summary for modular/modular: Delivered performance-oriented tooling and benchmarking capabilities that accelerate builds, improve benchmarking fidelity, and enhance data visualization. Key features include KBench CLI and Build/Performance Enhancements with parallel, CPU-aware builds; KBench Baseline Benchmarking with Empty Parameters for baseline comparisons; KPlot Plotting Tools Restored and Enhanced with Python-based plotting and profiling; Autotune Benchmarks Bazel Build Support with CI alignment; KProfile Enhancements and Differencing with dataclass refactor, pivots, and diffing; Bench Memcpy Config Serialization Enable Writable; and KBench BuildItem Robustness fixes to initialization. These contributions improve build speed, accuracy of performance measurements, and reliability of the benchmarking workflow, delivering clear business value in faster release cycles and better data-driven decisions.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 summary: Delivered a major KBench workflow overhaul in modular/modular, decoupling build and execution, introducing BuildItem and Scheduler to support parallel builds and caching, and refining UX with improved logging, progress display, and cache activation. The overhaul also standardizes naming (path to hash) for semantic clarity and fixes an internal Mojo utilities issue to improve reliability during benchmark runs. In addition, refactoring of kbench object-cache and main loop enhances stability and maintainability. These changes collectively enable faster, more reproducible benchmarks and reduce operational risk across environments.
3 Commits • 1 Features
Apr 1, 2025April 2025 summary: Delivered a major KBench workflow overhaul in modular/modular, decoupling build and execution, introducing BuildItem and Scheduler to support parallel builds and caching, and refining UX with improved logging, progress display, and cache activation. The overhaul also standardizes naming (path to hash) for semantic clarity and fixes an internal Mojo utilities issue to improve reliability during benchmark runs. In addition, refactoring of kbench object-cache and main loop enhances stability and maintainability. These changes collectively enable faster, more reproducible benchmarks and reduce operational risk across environments.
April 2025
March 2025
10 Commits • 5 Features
Mar 1, 2025Month: 2025-03 — Modular/modular: Focused performance tuning, benchmarking improvements, and tooling enhancements delivering measurable business value. Highlights include GPU matmul tuning for H100 and large tensor shapes; enhanced allreduce benchmarks; kplot and kbench tooling upgrades; ParamSpace improvements; Benchmark Mode utility; and a bug fix in bench_elementwise. These workstreams improved runtime performance, benchmarking fidelity, and developer productivity.
March 2025
10 Commits • 5 Features
Mar 1, 2025Month: 2025-03 — Modular/modular: Focused performance tuning, benchmarking improvements, and tooling enhancements delivering measurable business value. Highlights include GPU matmul tuning for H100 and large tensor shapes; enhanced allreduce benchmarks; kplot and kbench tooling upgrades; ParamSpace improvements; Benchmark Mode utility; and a bug fix in bench_elementwise. These workstreams improved runtime performance, benchmarking fidelity, and developer productivity.
Activity
Loading activity data...
Quality Metrics
Correctness85.8%
Maintainability83.4%
Architecture84.0%
Performance79.4%
AI Usage20.8%
Skills & Technologies
Programming Languages
BazelMarkdownMojoPythonStarlarkYAML
Technical Skills
Backend DevelopmentBazelBazel Build SystemBenchmarkingBug FixBug FixingBuild System ConfigurationBuild SystemsC++ (implied)CI/CDCLI DevelopmentCUDACUDA KernelsClickCode Generation
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline