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gpu-benchmarking

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jetson-orin-matmul-analysis

Cre4T3Tiv3 / jetson-orin-matmul-analysis

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Star 13

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

ZrobMiloudaa / jetson-orin-matmul-analysis

Star 1

🔍 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 2, 2026
  • Python

kadamrahul18 / GPT2-Optimization

Star 0

GPT-2 (124M) fixed-work distributed training benchmark on NYU BigPurple (Slurm) scaling 1→8× 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).

performance hpc amp slurm pytorch reproducibility distributed-training mixed-precision gpt2 deepspeed zero-1 gpu-benchmarking
  • Updated Jan 28, 2026
  • Python

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