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
During March 2025, Flam developed high-performance half-precision GEMM kernels for the ignaciosica/tinygrad repository, focusing on optimizing matrix multiplication on NVIDIA GPUs. Leveraging C++, CUDA, and low-level assembly (CUDA PTX), Flam implemented custom CUDA kernels that use 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, addressing the need for faster inference and training in deep learning applications. The work demonstrated depth in GPU optimization and low-level performance tuning, laying a robust foundation for improved cost-per-operation in future TinyGrad deployments.
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
87 people
Work 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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