Boosting Performance on NVIDIA Ampere Architecture: A Guide to Controlling Data Movement
Summary
The NVIDIA Ampere GPU architecture offers significant performance improvements over its predecessors, particularly in AI training and inference workloads. One key aspect of achieving these improvements is controlling data movement within the GPU. This article explores how developers can leverage the Ampere architecture’s features to optimize data movement and boost performance.
Introduction
The NVIDIA Ampere GPU architecture is designed to deliver faster performance for HPC, AI, and data analytics workloads. It builds upon the capabilities of the prior NVIDIA Tesla V100 GPU and introduces several new features that enhance performance and efficiency. One critical aspect of optimizing performance on the Ampere architecture is controlling data movement.
Understanding the Ampere Architecture
The Ampere GPU architecture includes several key features that improve performance and efficiency:
- Streaming Multiprocessor (SM): The new SM in the Ampere architecture significantly increases performance and adds new capabilities such as hardware acceleration for copying data from global memory to shared memory, split arrive/wait barriers, and improved tensor core operations.
- Asynchronous Data Copy: The Ampere GPU includes a new asynchronous copy instruction that loads data directly from global memory into SM shared memory, eliminating the need for intermediate register file usage. This reduces register file bandwidth, uses memory bandwidth more efficiently, and reduces power consumption.
- Third-Generation Tensor Cores: The Ampere architecture includes new third-generation Tensor Cores that are more powerful than those used in Volta and Turing SMs. These Tensor Cores support FP64, Bfloat16, and TF32 instructions, which accelerate processing of FP32 data and exploit fine-grained structured sparsity in deep learning networks.
Controlling Data Movement
Controlling data movement is crucial for optimizing performance on the Ampere architecture. Here are some strategies to achieve this:
- Asynchronous Copy: Use the asynchronous copy instruction to load data directly from global memory into SM shared memory. This eliminates the need for intermediate register file usage and reduces power consumption.
- L2 Cache Residency Controls: The Ampere architecture allows CUDA users to control the persistence of data in the L2 cache. This feature can be used to manage data and keep frequently accessed data in the cache.
- Shared Memory Capacity: The Ampere GPU increases the shared memory capacity per SM, which can be used to store more data locally and reduce memory accesses.
Optimizing for Performance
To optimize performance on the Ampere architecture, developers should consider the following strategies:
- Compile for Compute Capability 8.6: Devices of compute capability 8.6 have 2x more FP32 operations per cycle per SM than devices of compute capability 8.0. Compiling explicitly for 8.6 can benefit from the increased FP32 throughput.
- Use Third-Generation Tensor Cores: The new Tensor Cores in the Ampere architecture support FP64, Bfloat16, and TF32 instructions. Using these instructions can accelerate processing of FP32 data and exploit fine-grained structured sparsity in deep learning networks.
- Leverage NVLink: The third-generation NVLink interconnect implemented in A100 GPUs significantly enhances multi-GPU scalability, performance, and reliability. Using NVLink can improve GPU-GPU communication bandwidth and reduce latency.
Table: Key Features of NVIDIA Ampere Architecture
| Feature | Description |
|---|---|
| Asynchronous Data Copy | Loads data directly from global memory into SM shared memory, eliminating the need for intermediate register file usage. |
| Third-Generation Tensor Cores | Supports FP64, Bfloat16, and TF32 instructions, accelerating processing of FP32 data and exploiting fine-grained structured sparsity in deep learning networks. |
| L2 Cache Residency Controls | Allows CUDA users to control the persistence of data in the L2 cache, managing data and keeping frequently accessed data in the cache. |
| Shared Memory Capacity | Increases shared memory capacity per SM, storing more data locally and reducing memory accesses. |
| Third-Generation NVLink | Enhances multi-GPU scalability, performance, and reliability, improving GPU-GPU communication bandwidth and reducing latency. |
Table: Performance Improvements of NVIDIA Ampere Architecture
| Workload | Performance Improvement |
|---|---|
| AI Training | Up to 2x faster than V100 |
| AI Inference | Up to 2x faster than V100 |
| HPC Applications | Up to 1.5x faster than V100 |
| Data Analytics | Up to 1.5x faster than V100 |
Table: Key Specifications of NVIDIA A100 GPU
| Specification | Value |
|---|---|
| Compute Capability | 8.0 (A100), 8.6 (other GPUs) |
| Shared Memory Capacity per SM | 164 KB (A100), 100 KB (other GPUs) |
| L2 Cache Capacity | 40 MB |
| NVLink Bandwidth | 600 GB/s (bidirectional) |
| Tensor Core Operations | FP64, Bfloat16, TF32 |
Conclusion
The NVIDIA Ampere GPU architecture offers significant performance improvements over its predecessors, particularly in AI training and inference workloads. By controlling data movement and leveraging the architecture’s features, developers can optimize performance and achieve faster execution times. Key strategies include using asynchronous copy, managing L2 cache residency, and leveraging third-generation Tensor Cores and NVLink.