ROCm/flash-attention
Feb 2025 – Feb 2026
4 Months active
Languages Used
C++CUDAPython
Technical Skills
C++CUDA programmingMachine Learning AccelerationPerformance OptimizationCUDA ProgrammingDeep Learning Optimization
Ted Zadouri
PROFILE
Ted Zadouri
Ted Zadouri contributed to the ROCm/flash-attention repository by developing and optimizing GPU kernels for advanced attention mechanisms in deep learning models. Over four months, Ted implemented architecture-aware features and backward pass optimizations, including a two-CTA approach and enhanced concurrency control for Hopper and SM100 GPUs. His work involved refactoring CUDA kernels, improving memory management, and introducing synchronization modules to boost throughput and resource utilization. Using C++, CUDA, and Python, Ted focused on performance optimization and parallel computing, enabling faster transformer workloads and scalable large-model inference. The depth of his contributions reflects strong expertise in GPU programming and deep learning acceleration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
5,944
Activity Months4
Your Network
59 peopleWork History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ROCm/flash-attention: Delivered a backward-pass optimization using a two-CTA (Cooperative Thread Array) approach in Flash Attention, improving GPU throughput and efficiency for attention mechanisms. Changes focused on memory management and kernel execution flow to support the new architecture, enabling better resource utilization and faster processing of attention mechanisms. The work is documented in commit 710d3cc239eb5171e8b87bcde9e51349d4affe8b (BWD sm100 2cta #2202), with co-authorship attributed to root. No major bugs fixed were reported for this repository this month. This work contributes to faster transformer workloads, reduced latency, and better GPU resource utilization across clusters.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for ROCm/flash-attention: Delivered a backward-pass optimization using a two-CTA (Cooperative Thread Array) approach in Flash Attention, improving GPU throughput and efficiency for attention mechanisms. Changes focused on memory management and kernel execution flow to support the new architecture, enabling better resource utilization and faster processing of attention mechanisms. The work is documented in commit 710d3cc239eb5171e8b87bcde9e51349d4affe8b (BWD sm100 2cta #2202), with co-authorship attributed to root. No major bugs fixed were reported for this repository this month. This work contributes to faster transformer workloads, reduced latency, and better GPU resource utilization across clusters.
February 2026
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025: ROCm/flash-attention – focused on architectural kernel enhancements and GPU concurrency to improve performance and scalability for large models on SM100.
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025: ROCm/flash-attention – focused on architectural kernel enhancements and GPU concurrency to improve performance and scalability for large models on SM100.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ROCm/flash-attention: Key feature delivered was the backward pass for FlashAttention on SM90/Hopper using CUTe. This included refactoring postprocessing and introducing CUDA kernels to enable efficient backward computation on newer hardware. No major bugs fixed this month. Overall impact: enables reliable backward gradient computation on the latest Hopper SM90 GPUs, supporting higher training throughput and broader hardware compatibility; solid foundation for future optimizations. Technologies/skills demonstrated: CUDA kernel development, CUTe integration, hardware-aware backward-pass implementation, code refactoring for pipeline cohesion.
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ROCm/flash-attention: Key feature delivered was the backward pass for FlashAttention on SM90/Hopper using CUTe. This included refactoring postprocessing and introducing CUDA kernels to enable efficient backward computation on newer hardware. No major bugs fixed this month. Overall impact: enables reliable backward gradient computation on the latest Hopper SM90 GPUs, supporting higher training throughput and broader hardware compatibility; solid foundation for future optimizations. Technologies/skills demonstrated: CUDA kernel development, CUTe integration, hardware-aware backward-pass implementation, code refactoring for pipeline cohesion.
September 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ROCm/flash-attention: Delivered architecture-aware feature enhancement enabling latent=512 path for FA3 with rope=64 on Hopper GPUs. Implemented conditional path for latent=512 when rope=64, updated run_mha_fwd to utilize latent=512 for Hopper (Arch == 90) with head dimension dv > 64, and updated the HasQv macro in flash_fwd_launch_template.h to reflect the new configuration. Committed changes reflect MLA flag enablement for FA3 under rope=64, latent=512 (#1504). No explicit bugs fixed were reported for this period in the provided data.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for ROCm/flash-attention: Delivered architecture-aware feature enhancement enabling latent=512 path for FA3 with rope=64 on Hopper GPUs. Implemented conditional path for latent=512 when rope=64, updated run_mha_fwd to utilize latent=512 for Hopper (Arch == 90) with head dimension dv > 64, and updated the HasQv macro in flash_fwd_launch_template.h to reflect the new configuration. Committed changes reflect MLA flag enablement for FA3 under rope=64, latent=512 (#1504). No explicit bugs fixed were reported for this period in the provided data.
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture84.0%
Performance88.0%
AI Usage32.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
C++CUDACUDA ProgrammingCUDA programmingConcurrency controlCutlass library usageDeep LearningDeep Learning OptimizationGPU KernelsGPU ProgrammingGPU programmingHigh-Performance ComputingMachine LearningMachine Learning AccelerationParallel Computing
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