ROCm/aiter
Dec 2025 – Jul 2026
6 Months active
Languages Used
Python
Technical Skills
GPU ProgrammingMatrix MultiplicationPerformance BenchmarkingQuantizationDeep LearningMachine Learning
nsusanto
PROFILE
Nsusanto
Over six months, contributed advanced GPU programming and machine learning kernel development to the ROCm/aiter repository, focusing on scalable Mixture of Experts (MoE) workloads. Built and optimized quantized GEMM kernels, including a8w8 and a4w4 variants, leveraging Triton and Python to improve throughput, memory efficiency, and energy performance. Integrated features such as smooth quantization, asynchronous data movement, and support for large token sizes, while refactoring activation and reduce operations for maintainability. Enhanced benchmarking and profiling infrastructure, introduced conditional logic for hardware-specific optimizations, and maintained robust test coverage. The work enabled reliable, high-performance MoE inference on modern AMD architectures.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
6
Lines of code
9,195
Activity Months6
Your Network
1985 peopleSame Organization
@amd.com1733
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Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter focusing on delivering a high-impact feature that optimizes the MoE A8W4 GEMM kernel for GFX1250. The work centers on performance, memory efficiency, and test coverage to enable robust MoE workloads on AMD hardware.
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for ROCm/aiter focusing on delivering a high-impact feature that optimizes the MoE A8W4 GEMM kernel for GFX1250. The work centers on performance, memory efficiency, and test coverage to enable robust MoE workloads on AMD hardware.
July 2026
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for ROCm/aiter: Delivered Triton TDM integration for MoE metadata kernels, introducing a USE_TDM flag to enable conditional TDM logic and improve data loading and tensor operation performance on supported architectures. This work lays groundwork for scalable MoE workloads and better hardware utilization.
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 performance summary for ROCm/aiter: Delivered Triton TDM integration for MoE metadata kernels, introducing a USE_TDM flag to enable conditional TDM logic and improve data loading and tensor operation performance on supported architectures. This work lays groundwork for scalable MoE workloads and better hardware utilization.
April 2026
1 Commits • 1 Features
Apr 1, 2026Monthly performance summary for 2026-04 focusing on ROCm/aiter: delivered a feature refactor that improves performance and scalability, specifically SwiGLU activation and reduce operation with support for token sizes beyond 65k tokens and optional residual connections. Updated Triton-based kernels to align with the new interface and maintainability goals.
1 Commits • 1 Features
Apr 1, 2026Monthly performance summary for 2026-04 focusing on ROCm/aiter: delivered a feature refactor that improves performance and scalability, specifically SwiGLU activation and reduce operation with support for token sizes beyond 65k tokens and optional residual connections. Updated Triton-based kernels to align with the new interface and maintainability goals.
April 2026
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 Monthly Summary for ROCm/aiter: Delivered a cutting-edge int8 Mixture of Experts (MoE) kernel with smooth quantization, incorporating non-power-of-two active experts and preshuffling for weights. Updated benchmarking suite and performance tuning to validate throughput and energy efficiency gains, and completed targeted code cleanups for maintainability. This work strengthens MoE scalability and reliability in production workloads.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 Monthly Summary for ROCm/aiter: Delivered a cutting-edge int8 Mixture of Experts (MoE) kernel with smooth quantization, incorporating non-power-of-two active experts and preshuffling for weights. Updated benchmarking suite and performance tuning to validate throughput and energy efficiency gains, and completed targeted code cleanups for maintainability. This work strengthens MoE scalability and reliability in production workloads.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 accomplishments focused on performance optimization and benchmarking readiness for MoE workloads in ROCm/aiter. Delivered two major MoE kernel enhancements: a4w4 GEMM kernel and an a8w8 blockscale MoE, with performance improvements from quantization, XCD swizzle, and improved routing, plus profiling, benchmarking, and test infrastructure upgrades. Shipped via commits 9eecdecb0d43a3e5cf2c57e418256ea3b0a4cb85 and f600a109b127685e95ff56a0f8683c1720b3e5ec. Follow-ups added kernel name suffixes (layer1/layer2) for easier profiling and introduced a --num-weight-inits flag to improve benchmark averaging. To preserve reliability, a4w4 unit tests on MI300 were gated. Overall impact includes faster MoE throughput, improved benchmarking reproducibility, and enhanced profiling support across devices; demonstrated expertise in GPU kernel design, quantization, performance tuning, and instrumentation.
2 Commits • 1 Features
Jan 1, 2026January 2026 accomplishments focused on performance optimization and benchmarking readiness for MoE workloads in ROCm/aiter. Delivered two major MoE kernel enhancements: a4w4 GEMM kernel and an a8w8 blockscale MoE, with performance improvements from quantization, XCD swizzle, and improved routing, plus profiling, benchmarking, and test infrastructure upgrades. Shipped via commits 9eecdecb0d43a3e5cf2c57e418256ea3b0a4cb85 and f600a109b127685e95ff56a0f8683c1720b3e5ec. Follow-ups added kernel name suffixes (layer1/layer2) for easier profiling and introduced a --num-weight-inits flag to improve benchmark averaging. To preserve reliability, a4w4 unit tests on MI300 were gated. Overall impact includes faster MoE throughput, improved benchmarking reproducibility, and enhanced profiling support across devices; demonstrated expertise in GPU kernel design, quantization, performance tuning, and instrumentation.
January 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/aiter: Delivered a new MoE GEMM a8w8 kernel for Triton with unit tests and benchmarks, expanding support for quantized matrix multiplication and enabling efficient MoE workloads. The work included kernel definitions, utility functions, and performance testing scripts to characterize throughput on quantized data paths. No major bugs fixed this month; focus was on feature delivery, testing, and performance evaluation to drive reliability and scalability of MoE workflows.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/aiter: Delivered a new MoE GEMM a8w8 kernel for Triton with unit tests and benchmarks, expanding support for quantized matrix multiplication and enabling efficient MoE workloads. The work included kernel definitions, utility functions, and performance testing scripts to characterize throughput on quantized data paths. No major bugs fixed this month; focus was on feature delivery, testing, and performance evaluation to drive reliability and scalability of MoE workflows.
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Quality Metrics
Correctness82.8%
Maintainability80.0%
Architecture82.8%
Performance85.8%
AI Usage48.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ManagementDeep LearningGPU ProgrammingGPU programmingMachine LearningMachine Learning KernelsMatrix MultiplicationPerformance BenchmarkingPerformance OptimizationPyTorchPythonQuantizationTriton
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