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Yifan Sun

PROFILE

Yifan Sun

Yifan Sun expanded benchmark coverage and improved system reliability across the sarchlab/mgpusim and sarchlab/akita repositories. He added new CDNA3 benchmarks for BFS/NW, FFT, SPMV, and N-body, resolving kernel metadata and data race issues by refining struct layouts and adopting safer kernel paths. Using Go and Python, he enhanced CI pipelines, introduced DRAM modeling, and upgraded Akita’s SQLite driver to a pure Go implementation for better compatibility. His work focused on algorithm optimization, performance benchmarking, and system simulation, resulting in broader hardware support, more accurate performance models, and faster feedback loops for more reliable decision-making and development.

Overall Statistics

Feature vs Bugs

88%Features

Repository Contributions

29Total
Bugs
2
Commits
29
Features
14
Lines of code
83,057
Activity Months1

Work History

March 2026

29 Commits • 14 Features

Mar 1, 2026

March 2026 performance summary focused on expanding CDNA3 benchmark coverage, stabilizing critical paths, and strengthening CI and tooling to deliver measurable business value across MGPSim and Akita. Key work included expanding CDNA3 support for BFS/NW, FFT, SPMV, and N-body benchmarks, plus substantial fixes to CDNA3 struct layouts, FLAT offset decoding, and HSACO integration. Stencil2D CDNA3 page fault and kernel descriptor metadata were resolved, with acceptance tests added to validate end-to-end correctness. The N-body CDNA3 fix used a safer GCN3 kernel path to eliminate data races and simplify usage. In Ares, we introduced -bytes flag support, CLI flag for FFT benchmarking, FFT sub-MB sizes, and timing model sync for M2.1 benchmarks, along with a DRAM modeling update via simplebankedmemory and CI/linter improvements, increasing reliability and throughput of benchmarks. MI300A calibration achieved 16.4% WMAPE accuracy across 206 matched points, signaling improved timing and memory behavior models. Cross-repo, Akita upgraded the SQLite driver to glebarez/go-sqlite (pure Go) for better compatibility and performance. Overall impact: broader hardware coverage, higher correctness, faster feedback loops, and improved stability that directly supports faster decision-making and more reliable performance projections.

Activity

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Quality Metrics

Correctness93.0%
Maintainability83.4%
Architecture86.2%
Performance87.0%
AI Usage25.6%

Skills & Technologies

Programming Languages

C++GoJavaScriptJuliaPythonTypeScriptYAML

Technical Skills

Algorithm optimizationBenchmarkingCI/CDCUDACode QualityContinuous IntegrationDependency ManagementDevOpsGPU ProgrammingGPU architectureGPU programmingGPU simulationGitHub ActionsGoGo programming

Repositories Contributed To

2 repos

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

sarchlab/mgpusim

Mar 2026 Mar 2026
1 Month active

Languages Used

C++GoJuliaPythonYAML

Technical Skills

Algorithm optimizationBenchmarkingCI/CDCUDACode QualityContinuous Integration

sarchlab/akita

Mar 2026 Mar 2026
1 Month active

Languages Used

GoJavaScriptPythonTypeScript

Technical Skills

GoNode.jsbackend developmentdatabase managementtesting