What are some of the best practices and tools for code generation and optimization for GPUs?

1 Know your target GPU

Before you start writing your code, you need to understand the features and limitations of your target GPU. Different GPUs have different architectures, memory models, instruction sets, and capabilities. For example, some GPUs support double-precision floating-point operations, while others do not. Some GPUs have more cores, registers, or shared memory than others. Some GPUs have dedicated hardware for certain functions, such as tensor operations, while others rely on software emulation. Knowing your target GPU can help you choose the right programming model, language, and compiler for your code.

2 Use a high-level programming language

While it is possible to write code directly in assembly or low-level languages for GPUs, it is not recommended for most cases. Writing low-level code can be tedious, error-prone, and difficult to maintain and port. Instead, you should use a high-level programming language that can abstract away some of the complexities and details of GPU programming. For example, you can use CUDA, OpenCL, or SYCL to write code that can run on different GPUs with minimal changes. These languages provide built-in functions, libraries, and tools that can simplify your code and optimize it for GPUs.

3 Optimize your memory access

One of the most important factors that affect the performance of your code on GPUs is how you access the memory. GPUs have different types of memory, such as global, shared, constant, and local memory, each with different sizes, speeds, and scopes. You need to optimize your memory access patterns to reduce the latency and bandwidth consumption of your code. For example, you can use coalesced memory access, where adjacent threads access adjacent memory locations, to improve the throughput of global memory. You can also use shared memory, which is faster and closer to the cores, to cache frequently used data or synchronize threads within a block. You can also use constant memory, which is read-only and cached, to store data that does not change during the execution.

4 Parallelize and vectorize your code

Another way to improve the performance of your code on GPUs is to parallelize and vectorize your code. Parallelization means dividing your problem into smaller and independent subproblems that can be solved by multiple threads or blocks concurrently. Vectorization means using single instructions that can operate on multiple data elements simultaneously. Both techniques can exploit the parallel and high-dimensional nature of GPUs and increase the utilization of the cores and registers. For example, you can use CUDA threads and blocks to parallelize your code across the GPU grid. You can also use CUDA vector types, such as float4 or int2, to vectorize your code and reduce the number of instructions.

5 Use profiling and debugging tools

Finally, you need to use profiling and debugging tools to measure and improve the performance and correctness of your code on GPUs. Profiling tools can help you identify the bottlenecks, hotspots, and inefficiencies of your code, such as memory stalls, divergent branches, or synchronization overheads. Debugging tools can help you find and fix the errors, bugs, and anomalies of your code, such as memory leaks, race conditions, or invalid results. For example, you can use NVIDIA Nsight tools or AMD CodeXL tools to profile and debug your code on NVIDIA or AMD GPUs, respectively. These tools can provide you with detailed information, metrics, and visualizations of your code behavior and performance on GPUs.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?