HazyResearch/ThunderKittens
Oct 2024 – Mar 2025
5 Months active
Languages Used
C++CUDAPythonMakefileCudaShell
Technical Skills
CUDACUDA ProgrammingKernel DevelopmentParallel ComputingPerformance OptimizationPython Scripting
Aaryan0404
PROFILE
Aaryan0404
Aaryan contributed to HazyResearch/ThunderKittens by engineering high-performance GPU kernels and infrastructure for deep learning workloads, focusing on transformer attention and memory optimization. He modernized CUDA-based attention kernels with causal masking and synchronization, integrated PyTorch workflows, and implemented hardware-aware optimizations for Blackwell/B100 GPUs. His work included developing benchmarking frameworks, enhancing memory transfer efficiency, and expanding test coverage to ensure reliability and maintainability. Using C++, CUDA, and Python, Aaryan addressed core algorithm improvements, scheduling, and batching for variable sequence lengths, resulting in scalable, production-ready code that improved throughput, stability, and performance visibility for large-scale machine learning and transformer models.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
95Total
Bugs
4
Commits
95
Features
26
Lines of code
22,454
Activity Months5
Your Network
23 people
Work History
March 2025
20 Commits • 1 Features
Mar 1, 20252025-03 Monthly Summary: Focused on performance, reliability, and profiling for the ThunderKittens MHA decode path. Delivered a cohesive set of core kernel enhancements with batching, scheduling, and variable-length sequence handling, complemented by robust benchmarking tooling and cache/memory policy improvements. The work strengthens decode throughput, scales with data and hardware, and provides measurable performance insights for ongoing optimization.
20 Commits • 1 Features
Mar 1, 20252025-03 Monthly Summary: Focused on performance, reliability, and profiling for the ThunderKittens MHA decode path. Delivered a cohesive set of core kernel enhancements with batching, scheduling, and variable-length sequence handling, complemented by robust benchmarking tooling and cache/memory policy improvements. The work strengthens decode throughput, scales with data and hardware, and provides measurable performance insights for ongoing optimization.
March 2025
February 2025
31 Commits • 9 Features
Feb 1, 2025February 2025 monthly summary for HazyResearch/ThunderKittens: Delivered performance benchmarking capabilities and experiments across page sizes and multi-page scenarios, implemented core algorithm improvements with reductions and partial results while preserving backward compatibility, completed PyTorch integration and GPU tensor fill optimizations, and expanded test coverage with unit/e2e tests and a base test scaffold. Also progressed benchmarking framework enhancements and scheduler integration, with ongoing stabilization and targeted bug fixes (register spills reduction, serialization spills fix, WG PC). These efforts improved performance visibility, reliability, and production readiness for ML workloads.
February 2025
31 Commits • 9 Features
Feb 1, 2025February 2025 monthly summary for HazyResearch/ThunderKittens: Delivered performance benchmarking capabilities and experiments across page sizes and multi-page scenarios, implemented core algorithm improvements with reductions and partial results while preserving backward compatibility, completed PyTorch integration and GPU tensor fill optimizations, and expanded test coverage with unit/e2e tests and a base test scaffold. Also progressed benchmarking framework enhancements and scheduler integration, with ongoing stabilization and targeted bug fixes (register spills reduction, serialization spills fix, WG PC). These efforts improved performance visibility, reliability, and production readiness for ML workloads.
January 2025
15 Commits • 2 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for HazyResearch/ThunderKittens focusing on hardware-aware transformer attention and memory-transfer optimizations. Delivered two key features: Attention Kernel Modernization and Hardware-Optimized Transformer Attention, and Tensor-to-Register and Tile Memory Transfer Optimizations. Also fixed major issues in the memory transfer path and improved stability. The work unlocks higher throughput on Blackwell/B100 GPUs, reduces data movement bottlenecks, and strengthens the foundation for larger models. Demonstrated CUDA kernel development, memory tiling, async operations, and template-based code generation.
15 Commits • 2 Features
Jan 1, 2025January 2025 (2025-01) monthly summary for HazyResearch/ThunderKittens focusing on hardware-aware transformer attention and memory-transfer optimizations. Delivered two key features: Attention Kernel Modernization and Hardware-Optimized Transformer Attention, and Tensor-to-Register and Tile Memory Transfer Optimizations. Also fixed major issues in the memory transfer path and improved stability. The work unlocks higher throughput on Blackwell/B100 GPUs, reduces data movement bottlenecks, and strengthens the foundation for larger models. Demonstrated CUDA kernel development, memory tiling, async operations, and template-based code generation.
January 2025
November 2024
26 Commits • 12 Features
Nov 1, 2024November 2024 (HazyResearch/ThunderKittens) focused on stabilizing the codebase after a reorganization, expanding the API surface with tests, implementing feature improvements, boosting performance, and broadening hardware support with Torch-Compile workflows. Key outcomes include stabilizing the codebase via targeted reorg/revert fixes; API description with unit tests; fills feature with column layout enhancements; targeted performance tuning; expanded GPU support with 4090/A100 baselines and MH 4090; Torch Compile integration with baselines and reorg to enable optimized workflows. These efforts reduce integration risk, accelerate API delivery and testing, standardize performance benchmarks, and enhance maintainability for hardware-accelerated workloads.
November 2024
26 Commits • 12 Features
Nov 1, 2024November 2024 (HazyResearch/ThunderKittens) focused on stabilizing the codebase after a reorganization, expanding the API surface with tests, implementing feature improvements, boosting performance, and broadening hardware support with Torch-Compile workflows. Key outcomes include stabilizing the codebase via targeted reorg/revert fixes; API description with unit tests; fills feature with column layout enhancements; targeted performance tuning; expanded GPU support with 4090/A100 baselines and MH 4090; Torch Compile integration with baselines and reorg to enable optimized workflows. These efforts reduce integration risk, accelerate API delivery and testing, standardize performance benchmarks, and enhance maintainability for hardware-accelerated workloads.
October 2024
3 Commits • 2 Features
Oct 1, 2024Month: 2024-10 — Summary: Delivered critical kernel and UI improvements for HazyResearch/ThunderKittens. Key features: Mamba2 kernel enhancements with a synchronization fix and performance/configuration improvements; attention visualization asset refresh (attn.png) to align with current UI standards. Major bugs fixed: kernel synchronization issues and related stability improvements, contributing to more reliable builds and runtimes. Impact: improved kernel stability and performance, consistent UI visuals, and faster, more predictable deployments. Technologies/skills demonstrated: kernel development (C/C++), performance tuning, asset pipelines, and cross-functional collaboration across repo teams. Business value: enhanced runtime efficiency, stability, and user experience across the product.
3 Commits • 2 Features
Oct 1, 2024Month: 2024-10 — Summary: Delivered critical kernel and UI improvements for HazyResearch/ThunderKittens. Key features: Mamba2 kernel enhancements with a synchronization fix and performance/configuration improvements; attention visualization asset refresh (attn.png) to align with current UI standards. Major bugs fixed: kernel synchronization issues and related stability improvements, contributing to more reliable builds and runtimes. Impact: improved kernel stability and performance, consistent UI visuals, and faster, more predictable deployments. Technologies/skills demonstrated: kernel development (C/C++), performance tuning, asset pipelines, and cross-functional collaboration across repo teams. Business value: enhanced runtime efficiency, stability, and user experience across the product.
October 2024
Activity
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Quality Metrics
Correctness84.0%
Maintainability82.0%
Architecture81.2%
Performance80.2%
AI Usage21.4%
Skills & Technologies
Programming Languages
C++CUDACudaMakefilePythonShell
Technical Skills
Algorithm DesignArgument ParsingAssembly LanguageAssembly languageAttention MechanismsAttention mechanismsBenchmarkingBuild SystemsC++C++ Template MetaprogrammingCUDACUDA Kernel TestingCUDA KernelsCUDA ProgrammingCUDA programming
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