nsight-compute

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

Evidence-driven CUDA, CUTLASS, Triton and GPU workload optimization for ChatGPT · 使用 ChatGPT 驱动 GPU workload 性能优化

Query every CUDA device attribute without profiling, and benchmark kernels against your hardware's theoretical peak.

Hand-written LLM inference CUDA kernels for consumer Blackwell (RTX 5060 Ti / sm_120) — 1.05x vLLM's Marlin at batch-1 decode, with reproducible Nsight Compute evidence for every level.

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.

Reproducible, profiler-driven optimization studies covering CUDA kernels, deep-learning inference, multi-GPU communication and accelerated computing.

基于证据的 vLLM v0.26 Scheduler Trace、KV Cache 压力、Waiting HOL 策略与 GPU Profiling 实验

TornadoVM vs hand-written CUDA on an RTX 4090: runnable Java and CUDA C++ versions of every demo, kernel-time comparisons attributed to root cause with Nsight Compute counters, and three upstream bugs found along the way.

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

CACAO is an open-source system that automatically generates, validates, profiles, and optimizes CUDA kernels using LLMs, real GPU performance feedback and KTT-based autotuning.

A CUDA kernel playground for GEMM, Tensor Cores, Softmax, LayerNorm, validation, profiling, and performance benchmarking.

Profiling-driven micro-architectural case study & custom Triton GPU operators for batch-1 edge vision inference on NVIDIA Ada Lovelace.

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.

Kernel-only profiling workflow for CUDA and Triton kernels with Nsight Compute, standardized reports, visual analysis, and vendor-portable adapters.

手写 LLM 推理核心算子的 CUDA 实现与 NCU 性能剖析（Reduce / GEMM / Softmax / Norm）

CUDA FP32 GEMM optimization with loop unrolling, shared memory tiling, register tiling, benchmarking, and Nsight profiling.

Feeding the Tensor Cores: a dense FP16 GEMM for NVIDIA Ada (sm_89) at 96.5% of cuBLAS, and what it teaches about how each GPU generation handles async copy and Tensor Core issue.

Production-grade GPU kernel optimization suite for GEMM across C++20, CUDA (SRAM tiling, float4 SIMD), and WebGPU/WGSL with Nsight Compute profiling.

A hand-optimized CUDA attention kernel for RAG cross-encoder reranking, achieving ~25% lower attention latency than PyTorch SDPA on an NVIDIA T4.