GPGPU-Ch3-SIMT-Core:Instruction-and-Register-Data-flow

Complete notes can be found here (Mandarin) :

Ch3 SIMT Core Notes

Brief overview of chapter3:

Chapter 3 dives deeper into the architectural marvels of modern GPUs, specifically focusing on the Single Instruction, Multiple Threads (SIMT) core, and subsequently, the memory system. This chapter first unpacks the intricate dance GPUs perform to handle the enormous datasets involved in graphics rendering. Unlike their ancestors, which struggled to cache entire data sets like texture maps on-chip, today’s GPUs cleverly sidestep this issue. They do so by simultaneously running tens of thousands of threads, leveraging on-chip caches to drastically cut down on off-chip memory accesses. This is illustrated through the efficient caching of spatial locality found in adjacent pixel operations in graphical workloads.

The microarchitecture of the GPU pipeline is then unveiled, showing a sophisticated division between the SIMT frontend and the SIMD backend. This is where the magic happens, involving three crucial scheduling loops that work in harmony within a pipeline: the instruction fetch loop, instruction issue loop, and the register access scheduling loop. This setup ensures that while each thread has minimal on-chip memory, the collective power of thousands of threads can be harnessed efficiently, thanks to the effective use of caches.

A significant portion of the discussion is dedicated to explaining how modern GPUs achieve their computational prowess by smartly navigating through the challenges posed by data and instruction handling. This includes an exploration of how GPUs utilize SIMD architecture to perform operations across multiple data points simultaneously, despite presenting a more flexible programming model through technologies like CUDA and OpenCL. It elaborates on how GPUs fit alongside CPUs, detailing the orchestration required in allocating memory, transferring data, and launching computational kernels to ensure seamless operation.

One of the highlights of this chapter is the comparison between NVIDIA’s PTX and AMD’s Graphics Core Next ISA, providing insights into how different approaches to GPU instruction sets can influence programming and performance optimization. This discussion extends into the nuances of GPU instruction sets, shedding light on how high-level programming commands are translated into machine-understandable instructions that GPUs can execute efficiently.

The chapter doesn’t stop at instruction handling; it ventures into the realm of GPU memory management, detailing how GPUs manage memory through a mix of on-chip caches and off-chip memory accesses. This is crucial for understanding how GPUs handle the vast amounts of data required for tasks ranging from complex scientific computations to rendering the vivid graphics in video games.

In essence, Chapter 3 is a deep dive into the heart of GPU architecture, revealing the layers of complexity and innovation that enable GPUs to perform at the cutting edge of computational power. It’s a testament to the continuous evolution of GPU technology, highlighting how architectural decisions impact the capabilities and efficiency of GPUs in handling a wide array of computational tasks.