GPU Architecture for Parallel Computing

Are you curious about the hardware that powers everything from high-end gaming to cutting-edge artificial intelligence? Modern GPU architecture is the foundation for unlocking massive computational speed and efficiency. By the end of this course, you will have a deep, foundational understanding of the internal structure of a GPU, including its core components, memory types, and the principles that enable highly parallel processing. You will be able to read and understand technical specifications related to streaming multiprocessors, memory bandwidth, and execution pipelines. What you'll learn: * Understand the historical evolution of GPUs from dedicated graphics renderers to general-purpose parallel processors (GPGPU). * Learn the fundamental concepts of parallel processing, including SIMD, SIMT, and thread execution models. * Master the relationship between streaming multiprocessors and computational throughput in modern architectures. * Understand the complex memory hierarchy within a GPU, including the roles of global, shared, and constant memory. * Apply architectural knowledge to assess potential performance bottlenecks in parallel workloads and code. * Configure basic execution parameters, recognizing the trade-offs between latency and throughput in compute kernels. The course begins with core definitions and the historical context of GPGPU, then systematically explores the functional units, instruction pipelines, and various memory subsystems. We conclude with practical discussions on how architectural constraints influence software optimization. This course is designed specifically for beginners with no prior experience in hardware design or parallel programming. If you are interested in computer architecture, AI hardware, or high-performance computing, this is the perfect starting point. Start reading today and demystify the power of the graphics processor.