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. In this paper, 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. It is released as BSD-licensed open-source from https://github.com/passlab/CUDAMicroBench.git.
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Accelerating Datalog applications with cuDF.
Datalog, a bottom-up declarative logic programming language, has a wide variety of uses for deduction, modeling, and data analysis across application domains. Datalog can be efficiently implemented using relational algebra primitives such as join, projection and union. While there exist several multi-threaded and multi-core implementations of Datalog targeting CPU-based systems, our work makes an inroads towards developing a Datalog implementation for GPUs. We demonstrate the feasibility of a high-performance relational algebra backend for a subset of Datalog applications that can effectively leverage the parallelism of GPUs using cuDF. cuDF is a library from the Rapids suite that uses the NVIDIA CUDA programming model for GPU parallelism. It provides similar functionalities to Pandas, a popular data analysis engine. In this paper, we analyze and evaluate the performance of cuDF versus Pandas for two graph-mining problems implemented in Datalog, (1) triangle counting and (2) transitive-closure computation.
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- NSF-PAR ID:
- 10387678
- Date Published:
- Journal Name:
- 12th Workshop on Irregular Applications: Architectures and Algorithms
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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