Are modern GPUs considered to be RISC based or CISC based?

Are Modern GPUs RISC or CISC? Unpacking the Architecture of Graphics Processors

Explore the fundamental instruction set architectures (ISAs) of modern GPUs, comparing them to traditional CPUs and clarifying whether they lean towards RISC or CISC principles.

The debate between Reduced Instruction Set Computing (RISC) and Complex Instruction Set Computing (CISC) has long defined the architectural landscape of CPUs. However, when it comes to Graphics Processing Units (GPUs), the answer isn't as straightforward. Modern GPUs are highly specialized processors designed for parallel computation, a task that differs significantly from the general-purpose processing of CPUs. This article delves into the architectural nuances of GPUs to determine where they fit within the RISC/CISC spectrum.

Understanding RISC vs. CISC Fundamentals

Before we analyze GPUs, let's briefly recap the core tenets of RISC and CISC architectures:

• RISC (Reduced Instruction Set Computing): Characterized by a small, highly optimized set of simple, fixed-length instructions. Each instruction typically completes in a single clock cycle, and complex operations are built by combining multiple simple instructions. RISC processors often have many general-purpose registers and rely heavily on compilers to optimize code.

• CISC (Complex Instruction Set Computing): Features a large, varied set of instructions, including complex operations that can perform multiple low-level operations (like memory access, arithmetic, and register manipulation) in a single instruction. Instructions can be variable-length, and their execution might take multiple clock cycles. CISC aims to reduce the number of instructions per program, potentially simplifying compiler design but increasing hardware complexity.

The GPU's Unique Architectural Approach

GPUs are built for throughput, not latency. Their design prioritizes executing a massive number of simple operations in parallel, rather than quickly executing a single complex instruction. This fundamental difference drives their architectural choices. While a CPU might have a few powerful cores, a GPU boasts thousands of smaller, simpler cores (often called Streaming Processors or CUDA Cores) that operate in unison.

Modern GPUs employ a Single Instruction, Multiple Data (SIMD) or Single Instruction, Multiple Thread (SIMT) execution model. This means that many processing units execute the same instruction simultaneously on different pieces of data. This model naturally favors simpler instructions that can be efficiently broadcast and executed across a vast array of parallel units.

flowchart TD
    A[CPU Architecture] --> B{Instruction Set}
    B --> C[CISC: Complex, Variable-length, Multi-cycle]
    B --> D[RISC: Simple, Fixed-length, Single-cycle]
    E[GPU Architecture] --> F{Execution Model}
    F --> G[SIMD/SIMT: Many simple cores, Parallel execution]
    G --> H[Instruction Characteristics: Simple, Optimized for throughput]
    C --"Focus: Latency, General Purpose"--> A
    D --"Focus: Latency, General Purpose"--> A
    H --"Focus: Throughput, Parallel Compute"--> E
    A --"Traditional Comparison"--> E

Comparison of CPU and GPU Architectural Focus

GPU Instruction Sets: A Hybrid Perspective

While GPUs don't strictly adhere to either RISC or CISC, their instruction sets share more characteristics with RISC. GPU instructions are generally:

1. Simple and Atomic: They perform basic operations like addition, multiplication, memory load/store, and branching. Complex functions are typically broken down into sequences of these simpler instructions.
2. Fixed-Length (or nearly so): This simplifies instruction decoding and pipelining, crucial for parallel execution.
3. Many Registers: GPUs have a large number of registers per thread, reducing the need for frequent memory access, which is a hallmark of RISC.

However, it's not a pure RISC design. GPU instruction sets often include specialized instructions optimized for graphics and parallel computing tasks, such as texture sampling, fused multiply-add (FMA) operations, and bit manipulation. These specialized instructions, while still relatively simple, are tailored to the GPU's domain, making them more 'complex' than a generic RISC instruction set might imply.

Furthermore, the microcode layer present in some GPU designs, which translates higher-level GPU assembly into even simpler micro-operations, can be seen as a nod to CISC-like internal complexity, even if the exposed instruction set is RISC-like.

In conclusion, modern GPUs are best described as having a RISC-like instruction set architecture tailored for highly parallel, throughput-oriented computation. They leverage simple, fixed-length instructions and a large register file, much like RISC processors. However, their domain-specific optimizations and the sheer scale of their parallel execution model differentiate them significantly from traditional general-purpose CPUs, whether RISC or CISC.