Programming to achieve high performance for NVIDIA GPUs using CUDA has been known to be challenging. A GPU has hundreds or thousands of cores that a program must exhibit sufficient parallelism to achieve maximum GPU utilization. A system with GPU accelerators has a heterogeneous and deep memory system that programmers must effectively and correctly use to fully take advantage of the GPU’s parallelism capability.
We present CUDAMicroBench, a collection of fourteen microbenchmarks that demonstrate performance challenges in CUDA programming and techniques to optimize the CUDA programs to address these challenges. It also includes examples and techniques for using advanced CUDA features such as data shuffling between threads, dynamic parallelism, etc that can help users optimize the CUDA program for performance.
The microbenchmark can be used for evaluating the performance of GPU architectures, the memory systems of GPU itself and of the whole system architectures, and for evaluating the effectiveness of compiler and performance tools for performance analysis. It can be used to help users understand the complexity of heterogeneous GPU-accelerator systems through examples and guide users for performance optimization.
| Benchmark name | Pattern of Performance Inefficiency | Optimization techniques |
| Optimizing Kernels to Saturate the Massive Parallel Capability of GPUs | ||
| WarpDivRedux | Threads enter different branches when they encounter the control flow statement | Change the algorithm: take the warp size as the step |
| DynParallel | Workloads that require the use of nested parallelism such as those using adaptive grids | Use dynamic parallelism to allow the GPU to generate its own work |
| Conkernels | Launch multiple kernel instances on one GPU | Use concurrent kernels technique |
| TaskGraph | Provide a more effective model for submitting work to the GPU | Pre-define the task graph and run-repeatedly execution flow |
| Effectively Leveraging the Deep Memory Hierarchy Inside GPU to Maximize The Computing Capability of GPU for Kernel Execution | ||
| Shmem | The data need to be accessed serveral times | Use shared memory to store the data which needs to be accessed repeatly |
| CoMem | Stride or random access of array across threads which have uncoaleasced memory access | Consecutive memory access across threads |
| MemAlign | Mallocation has unaliged adress at the begninning | Use aligned malloc |
| GSOverlap | Global-shared memory copy takes much time | Use the new function memcpy_async in CUDA11 to acclerate the data transfer |
| Shuffle | The data exchange between threads | Use shuffle to enable threads in the same warp directly share part of their results between registers |
| BankRedux | Two or more threads access different locations of the same bank | Change the algorithm to avoid bank conflicts |
| Properly Arranging Data Movement Between CPU and GPU to Reduce the Performance Impact of Data Movement for the GPU Program | ||
| HDOverlap | Host-device memory copy takes much time | Use cudaMemcpyAsync function to acclerate the data transfer |
| ReadOnlyMem | Large amount of read-only data | Put read-only data in constant/texture memory to get a higher speed |
| UniMem | Low memory access density | Put the data in unified memory and only copy the necessary pages |
| MiniTransfer | Wrong data layout causes a large amount of useless data transfer between CPU and GPU | Change to the correct data layout to avoid useless data transfer |
- A collection of fourteen microbenchmarks,
- Exhibit performance challenges in CUDA programming,
- Exhibit techniques to optimize the CUDA program to address the challenges,
- Includes examples and techniques for using advanced CUDA features,
- Help users to optimize CUDA program for performance
To execute microBenchmark, NVIDIA GPU and CUDA are needed. Some new features such as dynamic parallelism and memcpy_async require the GPU to support CUDA11.
There are fourteen examples so far you can experiment. You can check the Makefile in each folder to see how they can be compiled and then excute it by using the .sh file.
Several benchmarks are derived from CUDA Samples, including GSOverlap, ConKernels and Taskgraph. Some header files required by these three benchmarks are stored in the common folder, which also comes from CUDA Samples.
Yi, Xinyao, David Stokes, Yonghong Yan, and Chunhua Liao. "CUDAMicroBench: Microbenchmarks to Assist CUDA Performance Programming." In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 397-406. IEEE, 2021. (LLNL-CONF-819919)
3-clause BSD License and Acknowledgement
Copyright (c) 2020 - 2021 HPCAS Lab (https://passlab.github.io) from University of North Carolina at Charlotte. All rights reserved. Funding for this research and development was provided by the National Science Foundation under award number CISE SHF-1551182 and CISE SHF-2015254. The development is also funded by LLNL under Contract DE-AC52-07NA27344 and LLNL-LDRD Program under Project No.18-ERD-006.