Scientific CUDA benchmarking framework: 4 implementations x 3 power modes x 5 matrix sizes on Jetson Orin Nano. 1,282 GFLOPS peak, 90% performance @ 88% power (25W mode), 99.5% accuracy validation, edge AI deployment guide.

machine-learning robotics cuda cublas scientific-computing matrix-multiplication high-performance-computing gpu-computing performance-optimization autonomous-systems edge-computing nvidia-jetson edge-ai embeded-systems tensor-cores ml-deployment jetson-orin-nano gpu-benchmarking power-efficiency-benchmark cuda-optimization

• Updated Oct 14, 2025
• Python

🔍 Analyze CUDA matrix multiplication performance and power consumption on NVIDIA Jetson Orin Nano across multiple implementations and settings.

machine-learning robotics cuda cublas matrix-multiplication high-performance-computing gpu-computing performance-optimization autonomous-systems edge-computing nvidia-jetson embeded-systems tensor-cores ml-deployment jetson-orin-nano gpu-benchmarking power-efficiency-benchmark cuda-optimization

• Updated Feb 9, 2026
• Python

benchHUB is a Python-based project to parse, aggregate, and visualize system and performance benchmarks. It includes a Streamlit dashboard to display and compare results.

mac benchmarking data-science machine-learning hardware leaderboard gpu-computing leaderboards performance-testing gpu-benchmark fastapi streamlit benchmarking-utility apple-silicon cpu-benchmarks gpu-benchmarking

• Updated Jan 27, 2026
• Python

GPT-2 (124M) fixed-work distributed training benchmark on NYU BigPurple (Slurm) scaling ×ばつ V100 across 2 nodes using DeepSpeed ZeRO-1 + FP16/AMP. Built a reproducible harness that writes training_metrics.json + RUN_COMPLETE.txt + launcher metadata per run, plus NCCL topology/log artifacts and Nsight Systems traces/summaries (NVTX + NCCL ranges).

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• Updated Feb 4, 2026
• Python