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Skill Guide

GPU Architecture & CUDA Programming

GPU Architecture & CUDA Programming is the discipline of understanding the parallel processing hardware design of Graphics Processing Units and writing software using NVIDIA's CUDA platform to leverage their massive computational throughput for general-purpose computing (GPGPU).

This skill is the engine behind modern AI/ML, high-performance computing (HPC), and scientific simulation, directly enabling faster model training, complex data analysis, and real-time rendering that drives competitive advantage and product innovation. Mastering it translates to building systems that are orders of magnitude more efficient than CPU-only solutions, directly impacting time-to-market and operational costs.
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15% Avg AI Risk

How to Learn GPU Architecture & CUDA Programming

Focus on understanding the fundamental hardware: CUDA Cores, Streaming Multiprocessors (SMs), warps, and the memory hierarchy (Global, Shared, L1/L2 Cache, Registers). Study the CUDA C++ programming model basics: kernels, thread hierarchy (grids, blocks, threads), and memory management (cudaMalloc, cudaMemcpy).
Move to optimizing kernels by minimizing memory latency: use shared memory for data reuse, coalesce global memory accesses, and understand occupancy. Analyze real kernels with NVIDIA Nsight Compute to identify bottlenecks. Common mistakes include ignoring warp divergence, excessive use of global memory, and poor thread block size selection.
Master asynchronous execution (streams, concurrent kernels), dynamic parallelism, and multi-GPU programming (NCCL, peer-to-peer). Architect solutions by profiling at the system level, aligning kernel launch configurations with the target GPU's SM count and memory bandwidth. Mentor teams on writing performance-portable code across GPU generations (e.g., Ampere vs. Hopper) and integrating with frameworks like Triton or CUTLASS.

Practice Projects

Beginner
Project

Implement and Optimize a Vector Addition Kernel

Scenario

You are tasked with accelerating a simple vector addition operation (`C = A + B`) on the GPU, replacing a slow CPU loop.

How to Execute
1. Write a basic CUDA kernel that assigns one thread per output element. 2. Measure performance using CUDA events or `nvprof`. 3. Optimize by ensuring memory coalescing and experiment with different thread block sizes (e.g., 256, 512 threads). 4. Profile with Nsight Compute to see memory throughput and occupancy.
Intermediate
Project

Accelerate a Matrix Multiplication Kernel

Scenario

Implement a tiled matrix multiplication kernel that uses shared memory to minimize global memory traffic, a core optimization for deep learning frameworks.

How to Execute
1. Design a tiling strategy that loads sub-matrices of A and B into shared memory. 2. Implement the kernel with proper synchronization (`__syncthreads()`). 3. Handle boundary conditions for non-divisible matrix dimensions. 4. Benchmark against a naive implementation and NVIDIA's cuBLAS library to measure your performance relative to the peak FLOPS of the GPU.
Advanced
Project

Develop a Multi-GPU Parallel Reduction

Scenario

Build a high-performance parallel reduction (sum) that scales across multiple GPUs on a single node, handling load balancing and communication.

How to Execute
1. Partition the input array across available GPUs. 2. Write an intra-GPU reduction kernel for each device. 3. Implement inter-GPU communication using CUDA Peer-to-Peer (P2P) memory copies or MPI. 4. Use NVIDIA's Nsight Systems to profile the entire multi-GPU pipeline, identifying communication bottlenecks and optimizing kernel overlap with data transfers using CUDA streams.

Tools & Frameworks

Development & Profiling Tools

NVIDIA CUDA Toolkit (nvcc, cuda-gdb)Nsight Systems (nsys)Nsight Compute (ncu)NVIDIA Nsight Visual Studio Edition

The core SDK for compiling and debugging CUDA code. Nsight Systems provides system-level performance analysis (CPU/GPU interaction, kernel timelines). Nsight Compute is for in-depth kernel profiling (memory latency, occupancy, instruction throughput).

Performance Libraries & Frameworks

cuBLAS (Linear Algebra)cuDNN (Deep Learning Primitives)CUTLASS (C++ Template Abstractions for CUDA)Thrust (Parallel Algorithms)

Use cuBLAS and cuDNN for production-grade, optimized implementations of standard operations. CUTLASS provides building blocks for writing high-performance custom kernels. Thrust offers a high-level, STL-like interface for common parallel patterns (sort, reduce, scan).

Careers That Require GPU Architecture & CUDA Programming

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