Streaming Multiprocessors (SMs)
At the heart of a Graphics Processing Unit (GPU) lies the concept of Streaming Multiprocessors (SMs), defining the core processing units responsible for the execution of tasks.
In NVIDIA’s architecture, these SMs comprise multiple CUDA (Compute Unified Device Architecture) cores, while in AMD’s architecture, they are referred to as Stream Processors. The essence of SMs lies in their concurrent operation, enabling the GPU to handle and execute multiple tasks simultaneously.
Each SM acts as a powerhouse, capable of performing a multitude of operations concurrently. The parallelism achieved through SMs is a fundamental characteristic of GPU architecture, making it exceptionally efficient in handling tasks that can be parallelized. This parallel processing capability is particularly advantageous in scenarios where tasks involve a vast number of repetitive calculations or operations.
Memory Hierarchy
The memory hierarchy of GPUs is a critical aspect that significantly influences their performance. GPUs come equipped with dedicated memory known as Video RAM (VRAM), specifically designed to store data essential for graphics processing. The efficiency of memory management directly impacts the overall performance of the GPU.
The memory hierarchy within a GPU includes different levels, such as global memory, shared memory, and registers. Global memory serves as the primary storage for data that needs to be accessed by all threads.
| Level | Type | Characteristics | Proximity to GPU Cores | Examples |
|---|---|---|---|---|
| Global | GDDR (Graphics DDR) | High capacity, moderate speed | Far | GDDR5, GDDR6, HBM (High Bandwidth Memory) |
| Device | GPU (Device) | On-chip, shared among all GPU cores | On-chip | Shared L2 cache, L1 cache |
| Shared | Shared Memory | On-chip, shared within a GPU block (thread block) | On-chip | Shared memory within a CUDA thread block |
| Texture | Texture Memory | Optimized for texture mapping and filtering | On-chip | Specialized for texture operations |
| Constant | Constant Memory | Read-only data shared among all threads | On-chip | Read-only data for all threads |
| L1 Cache | Level 1 Cache | Fast, private cache for each GPU core | On-chip | L1 cache for individual GPU cores |
| L2 Cache | Level 2 Cache | Larger, shared cache for all GPU cores | On-chip | L2 cache shared among all GPU cores |
| Registers | Register File | Fastest, private storage for individual threads | On-chip | Registers allocated to each thread |
Shared memory is a faster but smaller memory space that allows threads within the same block to share data. Registers are the smallest and fastest memory units residing on the GPU cores for rapid access during computation.
Efficient memory management involves optimizing the utilization of these memory types based on the specific requirements of tasks. It ensures that data is swiftly accessed, processed, and shared among different components of the GPU, contributing to enhanced overall performance.
Parallel Processing
Parallel processing stands as a cornerstone of GPU architecture, making it exceptionally well-suited for tasks that can be parallelized. In parallel processing, multiple operations are executed simultaneously, a capability harnessed through the presence of multiple cores within SMs.
What is GPU? Graphic Processing Unit
A Graphics Processing Unit (GPU) is a specialized electronic circuit in computer that speeds up the processing of images and videos in a computer system. Initially created for graphics tasks, GPUs have transformed into potent parallel processors with applications extending beyond visual computing. This in-depth exploration will cover the history, architecture, operation, and various uses of GPUs.
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