ignaciosica/tinygrad
Mar 2025 – Mar 2025
1 Month active
Languages Used
C++CUDA
Technical Skills
Assembly (CUDA PTX)CUDA ProgrammingGPU OptimizationLow-Level OptimizationMatrix Multiplication
Francis Lam
PROFILE
Francis Lam
Developed high-performance half-precision GEMM kernels for the ignaciosica/tinygrad repository, focusing on accelerating matrix multiplication on NVIDIA GPUs. Leveraging C++, CUDA, and low-level GPU optimization techniques, the work introduced custom CUDA kernels utilizing both 2-stage and 3-stage pipelines, swizzled memory access patterns, and direct accumulator-to-output writes. This approach maximized throughput and energy efficiency for FP16 matrix multiplication workloads, laying a foundation for faster inference and training in deep learning applications. The implementation addressed core performance bottlenecks in matrix operations, demonstrating depth in assembly-level CUDA PTX programming and a strong focus on optimizing cost-per-operation for modern GPU architectures.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,418
Activity Months1
Your Network
119 peopleWork History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused update for ignaciosica/tinygrad. Implemented High-Performance Half-Precision GEMM Kernels using custom CUDA kernels for FP16 matrix multiplication on NVIDIA GPUs. The work features multi-stage pipelines (2-stage and 3-stage), swizzled memory access patterns, and direct accumulator-to-output writes to maximize throughput and energy efficiency for matrix multiply workloads. This foundational optimization sets the stage for faster inference and training with TinyGrad on modern GPUs and supports stronger cost-per-operation improvements in DL workloads.
1 Commits • 1 Features
Mar 1, 2025March 2025 performance-focused update for ignaciosica/tinygrad. Implemented High-Performance Half-Precision GEMM Kernels using custom CUDA kernels for FP16 matrix multiplication on NVIDIA GPUs. The work features multi-stage pipelines (2-stage and 3-stage), swizzled memory access patterns, and direct accumulator-to-output writes to maximize throughput and energy efficiency for matrix multiply workloads. This foundational optimization sets the stage for faster inference and training with TinyGrad on modern GPUs and supports stronger cost-per-operation improvements in DL workloads.
March 2025
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Quality Metrics
Correctness80.0%
Maintainability60.0%
Architecture80.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDA
Technical Skills
Assembly (CUDA PTX)CUDA ProgrammingGPU OptimizationLow-Level OptimizationMatrix Multiplication
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