NumPy & SciPy for GPU

Hooked CUDA-related dynamic libraries by using automated code generation tools.

A safe Rust FFI binding for the NVIDIA® Tools Extension SDK (NVTX).

PyProf2: PyTorch Profiling tool

Julia bindings for NVTX, for instrumenting with the Nvidia Nsight Systems profiler

Simple timing routines to be used in codes which use MPI and possibly CUDA/OpenACC using NVTX markers

Thin pybind11 wrapper for NVTX wrappers -- with some bells and whistles attached.

Create NVIDIA NVTX ranges directly in VS Code, then profile with Nsight Systems without modifying source code.

Reproducible GPT-2 distributed-training benchmarks on 1-8 V100 GPUs using Slurm, PyTorch, DeepSpeed, NCCL, NVTX, and Nsight Systems.

🎬 Explore GPU training efficiency with FP32 vs FP16 in this modular lab, utilizing Tensor Core acceleration for deep learning insights.

A reproducible GPU benchmarking lab that compares FP16 vs FP32 training on MNIST using PyTorch, CuPy, and Nsight profiling tools. This project blends performance engineering with cinematic storytelling—featuring NVTX-tagged training loops, fused CuPy kernels, and a profiler-driven README that narrates the GPU’s inner workings frame by frame.

Profile-first ML systems project optimizing a multi-camera end-to-end driving model for hardware efficiency using PyTorch, CUDA streams, NVTX instrumentation, and Nsight Systems.

Header-only C++17 latency profiler for real-time CUDA loops. Per-stage distributions, deadline tracking, and Perfetto timelines

Profiling with Precision. Documenting with Style.