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Duncan Moss

PROFILE

Duncan Moss

Worked on the flashinfer-ai/flashinfer repository to enhance deep learning inference capabilities, focusing on GPU programming and kernel optimization using CUDA, C++, and Python. Delivered support for large head dimensions in attention kernels, expanded test coverage for reliability, and implemented production-ready CuTe DSL kernels for recurrent KDA on Blackwell hardware. Addressed compatibility issues with FMHA artifacts and improved kernel selection logic for scalable, low-latency inference. Resolved decoding bugs for GQA with large head dimensions and collaborated on refining softmax scaling and kernel registration. Emphasized robust validation, benchmarking, and cross-hardware compatibility to ensure stable, high-performance deployment across diverse model configurations.

Overall Statistics

Feature vs Bugs

50%Features

Repository Contributions

4Total
Bugs
2
Commits
4
Features
2
Lines of code
5,485
Activity Months3

Work History

June 2026

1 Commits

Jun 1, 2026

June 2026 performance-focused update for flashinfer: resolved GQA decoding issues with large head dimensions, improved kernel selection, and broadened test coverage; delivered reliable, scalable inference for headDim=512 configurations; co-authored by Duncan Moss.

May 2026

2 Commits • 1 Features

May 1, 2026

May 2026: Strengthened decoder performance and KDA capabilities for flashinfer-ai/flashinfer. Delivered production-ready CuTe DSL kernels for recurrent KDA on SM100 (Blackwell), fixed critical FMHA ABI/cubin alignment for newer hardware, and expanded test coverage and benchmarking to validate performance and stability. These changes improve latency, hardware compatibility, and scalability for deployment.

April 2026

1 Commits • 1 Features

Apr 1, 2026

April 2026 monthly summary focusing on feature expansion for TRTLLM attention kernels, validation coverage, and preparation for future kernel upgrades. Delivered larger head_dim support and strengthened test suites to improve enterprise reliability and performance, enabling broader model configurations and safer deployments.

Activity

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Quality Metrics

Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance85.0%
AI Usage55.0%

Skills & Technologies

Programming Languages

C++CUDAPython

Technical Skills

CUDAData StructuresDeep LearningGPU ProgrammingMachine LearningPyTorch

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

flashinfer-ai/flashinfer

Apr 2026 Jun 2026
3 Months active

Languages Used

CUDAPythonC++

Technical Skills

Deep LearningGPU ProgrammingPyTorchCUDAData StructuresMachine Learning