gpu
6 lessons tagged gpu: free, quiz-checked micro-lessons.
Profiling CUDA: Occupancy, Memory Coalescing, and Nsight
A working CUDA kernel is the start, not the finish. How to measure occupancy, spot uncoalesced loads and warp divergence, and read the three numbers in Nsight Compute that actually matter.
Shared Memory Tiling for Matrix Multiplication
Why naive matmul on a GPU is bandwidth-starved, and how tiling with __shared__ memory reduces global memory traffic by a factor of the tile size. The classic optimisation, with the kernel that demonstrates it.
Your First CUDA Kernel: Vector Addition End-to-End
The hello-world of CUDA, done properly. Allocate device memory, copy inputs, launch a kernel, copy results back, free, and check every return code. The full driver + kernel in one runnable file.
CUDA Memory Hierarchy: Global, Shared, Constant, Local, Registers
The five memory spaces a CUDA kernel can see and why they have wildly different speeds. Global vs shared vs constant vs local vs registers, coalesced access, bank conflicts, and a cheat-sheet table you'll actually reference.
CUDA Programming Model: Kernels, Threads, Blocks, and Grids
How CUDA carves a problem into a grid of blocks of threads. Host vs device code, the __global__ qualifier, the launch syntax, and how every thread figures out which slice of data it owns.
Parallel Computing Fundamentals: CPUs, GPUs, Latency vs Throughput
Why GPUs eat CPUs for breakfast on some workloads and choke on others. Serial vs parallel execution, Amdahl's law, the latency-vs-throughput trade-off baked into silicon, and a rule of thumb for when to reach for a GPU.