CUDA编程练习项目-Hands-on CUDA kernels and performance optimization, covering GEMM, FlashAttention, Tensor Cores, CUTLASS, quantization, KV cache, NCCL, and profiling.

This is my 🔥 100 Days of GPU — a wild, hands-on journey through CUDA/CUTLASS kernels, Triton spells, and PTX sorcery.

Profiling with NVIDIA Nsight Tools Bootcamp

References content from the OLCF CUDA Training Series. (https://github.com/olcf/cuda-training-series)

CUDA Samples and Nsight Guided Profiling Samples

This project demonstrates the integration of a CUDA kernel within an NVIDIA Holoscan application. It consists of two custom operators: one for memory allocation and data initialization, and another for executing the CUDA kernel. The application was profiled using Nsight systems and the kernel with Nsight compute

GPU-accelerated Number-Theoretic Transform for ZK-Proof generation. Targets the NTT bottleneck (91% of Groth16 prover time) via two CUDA optimizations: async double-buffered pipeline eliminating CPU-GPU transfer overhead, and IADD3-path Montgomery multiplication reducing finite-field instruction latency. BLS12-381, Ampere sm_86, Nsight-profiled.

University Project for "Computer Architecture" course (MSc Computer Engineering @ University of Pisa). Implementation of a Parallelized Nearest Neighbor Upscaler using CUDA.

Real-time CUDA physics engine for N-body gravity, SPH fluids, and rigid-body collisions. Uses shared-memory tiling, kernel fusion, and spatial hashing on RTX 4080/4090.

A comprehensive, hardware-agnostic GPU benchmarking suite that compares CUDA, OpenCL, and DirectCompute performance using identical workloads. Built from scratch with professional architecture, extensive documentation, and production-ready GUI.

Julia tools for NVIDIA Nsight Compute

libHPC is a high-performance computing library focused on Linux and Windows environments. It provides SIMD-optimized kernels, concurrent data structures, GPU utilities, and HPC-oriented memory management components.

Roofline profiling for Deep Learning models