An official website of the United States government
In the past decade, GPUs have become an important resource for compute-intensive, general-purpose GPU applications such as machine learning, big data analysis, and large-scale simulations. In the future, with the explosion of machine learning and big data, application demands will keep increasing, resulting in more data and computation being pushed to GPUs. However, due to the slowing of Moore’s Law and rising manufacturing costs, it is becoming more and more challenging to add compute resources into a single GPU device to improve its throughput. As a result, spreading work across multiple GPUs is popular in data-centric and scientific applications. For example, Facebook uses 8 GPUs per server in their recent machine learning platform. However, research infrastructure has not kept pace with this trend: most GPU hardware simulators, including gem5, only support a single GPU. Thus, it is hard to study interference between GPUs, communication between GPUs, or work scheduling across GPUs. Our research group has been working to address this shortcoming by adding multi-GPU support to gem5. Here, we discuss the changes that were needed, which included updating the emulated driver, GPU components, and coherence protocol.
more »
« less
Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis
Genomic analysis is the study of genes which includes the identification, measurement, or comparison of genomic features. Genomics research is of great importance to our society because it can be used to detect diseases, create vaccines, and develop drugs and treatments. As a type of general-purpose accelerators with massive parallel processing capability, GPUs have been recently used for genomics analysis. Developing GPU-based hardware and software frameworks for genome analysis is becoming a promising research area. To support this type of research, benchmarks are needed that can feature representative, concurrent, and diverse applications running on GPUs. In this work, we created a benchmark suite called Genomics-GPU, which contains 10 widely-used genomic analysis applications. It covers genome comparison, matching, and clustering for DNAs and RNAs. We also adapted these applications to exploit the CUDA Dynamic Parallelism (CDP), a recent advanced feature supporting dynamic GPU programming, to further improve the performance. Our benchmark suite can serve as a basis for algorithm optimization and also facilitate GPU architecture development for genomics analysis.
more »
« less
- PAR ID:
- 10415345
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
- ISBN:
- 979-8-3503-9739-0
- Subject(s) / Keyword(s):
- genomics, bioinformatics, benchmarking, GPU, accelerated computing, genome analysis, computer architecture.
- Format(s):
- Medium: X
- Location:
- Raleigh, NC, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Block-Adaptive-Tree Solar-wind Roe-type Upwind Scheme (BATSRUS), our state-of-the-art extended magnetohydrodynamic code, is the most used and one of the most resource-consuming models in the Space Weather Modeling Framework. It has always been our objective to improve its efficiency and speed with emerging techniques, such as GPU acceleration. To utilize the GPU nodes on modern supercomputers, we port BATSRUS to GPUs with the OpenACC API. Porting the code to a single GPU requires rewriting and optimizing the most used functionalities of the original code into a new solver, which accounts for around 1% of the entire program in length. To port it to multiple GPUs, we implement a new message-passing algorithm to support its unique block-adaptive grid feature. We conduct weak scaling tests on as many as 256 GPUs and find good performance. The program has 50%–60% parallel efficiency on up to 256 GPUs and up to 95% efficiency within a single node (four GPUs). Running large problems on more than one node has reduced efficiency due to hardware bottlenecks. We also demonstrate our ability to run representative magnetospheric simulations on GPUs. The performance for a single A100 GPU is about the same as 270 AMD “Rome” CPU cores (2.1 128-core nodes), and it runs 3.6 times faster than real time. The simulation can run 6.9 times faster than real time on four A100 GPUs.more » « less
-
The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, which shifts the complex memory management from programmers to GPU driver/ hardware and enables kernel execution even when memory is oversubscribed. Meanwhile, UVM may also incur considerable performance overhead due to tracking and data migration along with special handling of page faults and page table walk. As UVM is attracting significant attention from the research community to develop innovative solutions to these problems, in this paper, we propose a comprehensive UVM benchmark suite named UVMBench to facilitate future research on this important topic. The proposed UVMBench consists of 32 representative benchmarks from a wide range of application domains. The suite also features unified programming implementation and diverse memory access patterns across benchmarks, thus allowing thorough evaluation and comparison with current state-of-the-art. A set of experiments have been conducted on real GPUs to verify and analyze the benchmark suite behaviors under various scenarios.more » « less
-
Abstract Background Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. Results In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. Conclusions ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.more » « less
-
The demand for high-performance computing resources has led to a paradigm shift towards massive parallelism using graphics processing units (GPUs) in many scientific disciplines, including machine learning, robotics, quantum chemistry, molecular dynamics, and computational fluid dynamics. In earthquake engineering, artificial intelligence and data-driven methods have gained increasing attention for leveraging GPU-computing for seismic analysis and evaluation for structures and regions. However, in finite-element analysis (FEA) applications for civil structures, the progress in GPU-accelerated simulations has been slower due to the unique challenges of porting structural dynamic analysis to the GPU, including the reliance on different element formulations, nonlinearities, coupled equations of motion, implicit integration schemes, and direct solvers. This research discusses these challenges and potential solutions to fully accelerate the dynamic analysis of civil structural problems. To demonstrate the feasibility of a fully GPU-accelerated FEA framework, a pilot GPU-based program was built for linear-elastic dynamic analyses. In the proposed implementation, the assembly, solver, and response update tasks of FEA were ported to the GPU, while the central-processing unit (CPU) instructed the GPU on how to perform the corresponding computations and off-loaded the simulated response upon completion of the analysis. Since GPU computing is massively parallel, the GPU platform can operate simultaneously on each node and element in the model at once. As a result, finer mesh discretization in FEA will not significantly increase run time on the GPU for the assembly and response update stages. Work remains to refine the program for nonlinear dynamic analysis.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISPASS57527.2023.00026
