Why Each CUDA Stream Can Execute Commands Independently: A Detailed Explanation Based on A100 GPU Architecture and CUDA SDK Functionality
The independent execution of commands in each CUDA stream is made possible by the interaction between the architectural design of the NVIDIA A100 GPU and the functionality of the CUDA SDK. Together, they efficiently manage GPU resources and maximize parallelism. Below is a detailed explanation:
One-Person AI Startup DeepNetwork CEO / SeokWeon Jang / sayhi7@daum.net
1. Architectural Features of the A100 GPU
The A100 GPU is built on NVIDIA's Ampere architecture and includes several features that enable concurrency and parallelism, forming the foundation for CUDA streams:
(1) Independent Execution by Streaming Multiprocessors (SMs)
The A100 GPU has dozens of SMs, each capable of independently executing thread blocks. SMs are the basic units for parallel processing, and their independence allows the following:
- Scheduling: Each SM independently schedules and executes thread blocks assigned to it. This means kernels assigned to different streams can execute on separate SMs concurrently.
- Independent Resource Allocation: Each SM has its own registers, shared memory, and warp schedulers, ensuring no interference between streams while executing tasks in parallel.
(2) Independent DMA Engines (Direct Memory Access)
The A100 GPU includes multiple DMA engines that allow memory transfers between the GPU and host to occur independently and asynchronously from kernel execution. These DMA engines enable:
- Asynchronous memory transfers for different streams.
- Overlapping of memory copy operations and kernel executions, improving overall throughput.
(3) Multi-Instance GPU (MIG) Capability
The A100 GPU supports MIG, which allows a single GPU to be divided into multiple virtual GPUs. Each virtual GPU operates with independent resources and scheduling, further enhancing concurrent stream execution. This feature is especially useful in high-performance computing environments.
2. Interaction Between CUDA SDK and A100 Hardware
The CUDA SDK provides software tools that allow developers to utilize A100 GPU resources effectively. Key aspects of this interaction include:
(1) Stream Concept in CUDA
In CUDA, a stream acts as a queue for execution commands. Commands within a stream execute sequentially, but commands in different streams can execute concurrently. The principles are as follows:
- Default Stream: The default stream executes commands sequentially, waiting for previous commands to complete before starting new ones.
- Asynchronous Streams: Developers can create multiple streams to enable asynchronous execution. Commands in these streams operate independently, allowing overlapping execution across streams.
(2) CUDA Runtime and Driver APIs
- CUDA Runtime API: This high-level API simplifies GPU resource management for developers. It supports stream creation, kernel execution, and memory transfer, enabling efficient asynchronous workflows.
- CUDA Driver API: This lower-level API directly interacts with GPU hardware, providing fine-grained control over SMs and DMA engines. Together, these APIs facilitate parallel execution.
(3) Concurrency and Scheduling
The CUDA scheduler manages the allocation of commands from different streams to GPU resources:
- Warp-Level Scheduling: Tasks are divided into warps (groups of 32 threads) and distributed among SMs.
- Multi-Stream Support: CUDA schedulers can handle multiple streams simultaneously, allowing kernel execution in one stream and memory transfers in another stream to overlap.
(4) Asynchronous Execution and Synchronization
CUDA offers APIs like cudaMemcpyAsync for asynchronous memory transfers, enabling independent execution of tasks across streams. Synchronization mechanisms such as cudaStreamSynchronize allow developers to wait for the completion of all tasks in a specific stream when needed.
3. Foundation for Concurrency and Parallelism
The combination of A100 GPU’s architectural design and CUDA SDK functionality enables parallelism and concurrency in the following ways:
(1) Hardware Independence
- A100’s SMs and DMA engines operate independently, allowing commands from different streams to execute on separate resources without interference.
(2) Software-Level Asynchronous Management
- CUDA SDK uses streams to manage kernel execution and memory transfers asynchronously, efficiently allocating hardware resources for concurrent execution.
Conclusion
The independent design of SMs and DMA engines in the A100 GPU, combined with the CUDA SDK’s ability to manage asynchronous commands, enables streams to execute kernels and perform memory transfers concurrently. This maximizes GPU performance, reduces execution time, and enhances resource efficiency.