{ "item_title" : "CUDA Programming in 21 Days", "item_author" : [" Mohammad Saqib "], "item_description" : "Twenty-one days. CUDA C++ is the spine; Python rides beside it the whole way. Every speed claim is measured, not just described. A CUDA kernel that launches with no error is not one that ran correctly: CUDA errors are asynchronous and silent unless checked by hand. The bottleneck is rarely the arithmetic -- it is the PCIe link. Day 1 sets a NumPy loop beside a CuPy call, no kernel written yet, to show what a GPU buys and what the transfer costs. By Day 9 memory access is coalesced across a 32-thread warp instead of fought line by line. By Day 15 a naive reduction becomes a three-stage tiled one, benchmarked against numpy.sum. Day 21 closes with a performance checklist and a project brief of your own. 234 figures across 21 chapters, each a real measurement or hardware schematic. 187 listings in CUDA C++, CuPy or Numba, and every printed output captured from a real run on an RTX 4070. Every idea appears in more than one language, so a reader stuck in one can read the other. YOUR 21-DAY PATH Day 1 - Why a Different Kind of Chip? Parallel Thinking From the Ground Up: throughput versus latency, and a first measured speedup.Day 2 - Setting Up, Toolkit, Driver, and Your First Run: nvidia-smi, nvcc, and a device query.Day 3 - Your First Kernel: __global__, the launch syntax, and SAXPY in CUDA C++, Numba and CuPy.Day 4 - Threads, Blocks, and Grids: threadIdx, blockDim, the global-index formula, the grid-stride loop.Day 5 - Vector Addition, End to End: the five steps of every CUDA program, cudaMalloc to a checked result.Day 6 - Moving Data Between Host and Device: pinned memory, measured bandwidth, unified memory.Day 7 - When Kernels Go Wrong, Error Checking and Debugging: cudaGetLastError, compute-sanitizer, a bug hunt.Day 8 - The Memory Hierarchy: global, shared and constant memory, and a measured latency ladder.Day 9 - Coalesced Memory Access: the 32-thread warp, strided versus contiguous access, array-of-structs.Day 10 - Shared Memory and Tiling: shared memory as scratchpad, bank conflicts, a tiled matrix transpose.Day 11 - Synchronization and Race Conditions: __syncthreads, a raced reduction, and why blocks cannot sync.Day 12 - Warps and Divergence: lockstep execution, branch divergence, and a warp-shuffle reduction.Day 13 - Occupancy and Launch Configuration: registers, shared memory, and the block-size sweet spot.Day 14 - Measuring, Timing, Events, and a First Roofline: CUDA events, Nsight, the roofline model.Day 15 - Reduction, the First Hard Kernel: tree reduction in shared memory, and a three-stage speedup ladder.Day 16 - Atomics and a Histogram: atomicAdd, contention, and a privatized histogram.Day 17 - Scan, Prefix Sum: inclusive and exclusive scan, and the Blelloch up-sweep, down-sweep.Day 18 - Streams and Overlap: cudaMemcpyAsync, pinned memory, and a double-buffered pipeline.Day 19 - Standing on Libraries, Thrust, cuBLAS, cuFFT, and CuPy: why a library beats a hand-rolled kernel.Day 20 - A Real Application, Image Convolution on the GPU: a tiled kernel, verified against SciPy.Day 21 - Wrapping Up and Where to Go Next: a performance checklist, the measure-first mindset, a project brief. WHO IT IS FOR. A programmer who knows a little C or C++ and a little Python, and wants to write, debug and profile a GPU kernel rather than just call one. You need a C/C++ compiler and Python with NumPy; a local GPU helps but is not required, since CuPy and Numba carry the ideas on a cloud notebook. No prior parallel programming, and no framework beyond the CUDA Toolkit. Download book source code from: {GitHub}/{mohammadnsaqib} 436 pages, black and white.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/818/377/9798183777802_b.jpg", "price_data" : { "retail_price" : "29.99", "online_price" : "29.99", "our_price" : "29.99", "club_price" : "29.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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