The Complete Course of CUDA Programming

Explore how grids, blocks, and threads drive CUDA programming, learn to compute a unique global thread id with blockIdx.x, blockDim.x, and threadIdx.x, and preview environment setup.

Install and configure the CUDA toolkit and Nvidia GPU drivers, set up Visual Studio, create a CUDA project with a runtime kernel, and run a simple add kernel.

Learn to implement cuda-based array addition in C by allocating memory for two input arrays and a result, performing element-wise sums on the gpu, and printing the results.

Explore a practical C code overview for CUDA programming: set up headers and a size macro, allocate five-element int arrays, implement a pointer-based add function, and print results.

Explore how to manage host and device memory in CUDA, including memory allocation, Cudamalloc and Cudamemcpy, and launching kernels for parallel computation.

Learn to write your first cuda code to parallelize salary updates, using the employee salary.h header, and apply 15% increases plus ₹5,000 as a new year's gift with cudamemcpy.

Master cuda debugging by implementing a host-device helper function, validating cuda calls, handling cuda errors, and ensuring safe memory management from allocation to reset.

Measure GPU and CPU activities in CUDA apps with Nvidia profiler to identify bottlenecks and boost performance. Explore nvprof and Nvidia Visual Profiler for visualizing kernel timing and memory copies.

Explore thread synchronization in CUDA programming, including locks, barriers, and atomic operations such as atomic add, atomic exchange, atomic minimum, atomic maximum, and atomic compare-and-swap to prevent race conditions.

Explore memory coalescing in CUDA programming by mapping threads to consecutive data for sequential access. Compare strided, random, and gather and scatter patterns to optimize bandwidth and minimize bank conflicts.

Learn to optimize memory transfers in cuda programming by overlapping computations and asynchronous data transfers, using non-blocking APIs, streams, and pipelining to hide memory latency.

Learn to use constant memory with __constant__ and cudaMemcpyToSymbol to transfer data from host to device, and create and manage texture memory via cuda texture objects and resource descriptors.

Learn to manage large-scale datasets with CUDA by optimizing memory usage, selecting appropriate memory types, partitioning data, overlapping transfers with computation, and debugging for performance.

Manage multiple GPUs and enable direct peer-to-peer communication using cudaSetDevice, get device count, and cudaMemcpyPeerAsync. Learn to synchronize with streams for overlapping transfers and kernels.

Utilize CUDA libraries such as cuBLAS, cuFFT, cuDNN, and curand to accelerate parallel algorithms on GPUs, enabling high-performance linear algebra, random number generation, and deep learning primitives.

Discover dynamic parallelism in cuda, enabling kernels to launch child kernels and perform recursive calls within gpu. See factorial and fibonacci examples illustrate recursive gpu programming with grids and threads.

Learn to optimize CUDA programs by understanding warp divergence, predication to avoid branching bottlenecks, and memory access patterns such as coalesced access, data reuse, and loop tiling for better throughput.

Profile CUDA applications using the command-line profiler and NVIDIA Visual Profiler, analyze GPU activities, API calls, memory transfers, and kernel timing to optimize dynamic parallelism and kernels.

Fine-tune CUDA kernels to maximize parallelism and throughput by balancing threads, memory access patterns, and memory types, using coalesced access, shared memory, and registers while avoiding excess synchronization.

Identify performance bottlenecks with a profiler to locate hotspots in Cuda kernels, then optimize by parallelizing work and using coalesced memory access patterns.

Explore CUDA applications across scientific computing, machine learning, and data analytics, with case studies in tsunami modeling (Anuga) and molecular visualization (VMD), showcasing GPU-accelerated simulations and deep learning pipelines.

Build a real-time CUDA project by initializing random arrays a, b, c and computing d = a*b + c with a kernel, using the curand library, streams, and host-device memory.

Celebrate completing the CUDA programming course by reviewing dynamic parallelism, CUDA basics, memory allocation and transfer, and synchronization, and apply CUDA to parallelize many applications with NumPy in Python.