A curated list of resources for mastering GPU engineering from architecture and kernel programming to large-scale distributed systems and AI acceleration.

• Programming Massively Parallel Processors: A Hands-on Approach — David B. Kirk & Wen-mei W. Hwu The canonical introduction to CUDA, memory hierarchies, and parallel patterns. Amazon , notes: Abi's Concise Notes
• CUDA by Example — Jason Sanders & Edward Kandrot
  A practical introduction to CUDA for beginners. Amazon
• The Ultra-Scale Playbook: Training LLMs on GPU Clusters - Hugging Face Web Version

• CUDA — NVIDIA’s proprietary GPU programming platform.
  • Libraries: cuBLAS, cuDNN
• ROCm — AMD’s open compute stack.
• OpenCL — Cross-platform parallel computing standard.
• SYCL / oneAPI — Intel’s C++ abstraction for heterogeneous compute.
• Vulkan Compute — Low-level GPU compute API.
• Kompute — Higher level general purpose GPU compute framework built on Vulkan.
• Metal Performance Shaders — Apple’s GPU framework.

• NVIDIA Nsight Systems — System-wide GPU profiler.
• Nsight Compute — Kernel-level performance analysis.
• Occupancy Calculator — NVIDIA spreadsheet for kernel configuration.
• CUTLASS — CUDA templates for linear algebra subroutines.
• TensorRT — High-performance deep learning inference.
• OpenAI Triton — Python DSL for writing high-performance GPU kernels.
• Roofline Model — Analytical model to reason about compute/memory bottlenecks.

• NVIDIA Ampere Whitepaper
• AMD RDNA & CDNA Architectures
• SIMT execution and warp scheduling
• Memory hierarchy and coalescing
• Shared memory and cache optimization
• Warp divergence and thread occupancy

• NCCL — Multi-GPU communication primitives.
• vLLM - Inference and serving engine for LLMs
• Hugging Face Accelerate - Simplify abstractions for distributed training
• SGLang
• Prime Intellect
• TensorRT-LLM
• TGI by Hugging Face
• Horovod — Distributed deep learning across GPUs.
• NVLink & PCIe Topology — GPU interconnects and bandwidth optimization.
• GPUDirect RDMA — Zero-copy GPU networking.
• Ray Train, DeepSpeed, Megatron-LM — Large-scale GPU orchestration frameworks.
• Iris by AMD - open-source multi-GPU programming framework built for compiler-visible performance and optimized multi-GPU execution.

• CUDA C++ Programming Guide
• Triton Tutorials (OpenAI)
• CUDA in 12 hours by FreeCodeCamp and Video Repo
• Stanford CS149, Fall 2025 Parallel Computing Course Fall 2025
• CMU 15-418/618: Parallel Computer Architecture & Programming
• MIT 6.5940: TinyML and Efficient Deep Learning Computing
• GPU MODE video lecture series
• Red Hat vLLM Office Hours video series

• Optimization techniques for GPU programming - Hijma, Pieter, et al.
• Efficient Multi-GPU Programming in Python: Reducing Synchronization and Access Overheads - Oden, Lena, and Klaus N��lp
• Evolving GPU Architecture — Kirk & Hwu
• Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision - Wei Gao et al
• Optimizing Machine Learning Models with CUDA: A Comprehensive Performance Analysis - Niteesh, L., and M. B. Ampareeshan
• NVIDIA Research Papers on Model Parallelism and Megatron-LM
• GPU Virtualization and Multi-Tenant Scheduling
• A Survey of Multi-Tenant Deep Learning Inference on GPU
• Efficient Performance-Aware GPU Sharing with Compatibility and Isolation through Kernel Space Interception

• nvprof, nvvp, Nsight Systems / Compute — NVIDIA profiling tools.
• cuda-memcheck, compute-sanitizer — Memory and correctness tools.
• GPGPU-Sim, Accel-Sim — GPU simulation frameworks.
• Perfetto, Nsight UI — Visual profilers for tracing GPU workloads.

• LeetGPU
• GPU MODE Discord
• GPU Glossary - A dictionary of terms related to programming GPUs

• PyTorch CUDA Extensions — Custom kernels for PyTorch.
• JAX + XLA — Compiler-based GPU vectorization.
• TensorFlow XLA Compiler — Ahead-of-time GPU graph compilation.
• FlashAttention, FlashConv — Kernel optimization techniques for transformers.
• DeepSpeed, FSDP, Megatron-LM — Distributed training systems.

• FlashAttention and PagedAttention
• Matmul Operations
• GPU scheduling algorithms and runtime systems.
• Memory oversubscription and unified memory models.
• Resource allocation in GPU clusters.
• GPU virtualization
• Kernel fusion and graph execution
• Dataflow optimization
• Persistent threads model

Contributions welcome!
Please read the contribution guidelines before submitting a pull request.

CC BY 4.0 — feel free to share and adapt with attribution.

Inspired by:

• Awesome HPC
• Awesome Computer Architecture
• Awesome CUDA

“GPU engineering is not just about writing kernels. It’s about understanding how systems work.” — Model Craft