FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications
arXiv:2106.06433 · doi:10.1109/MM.2021.3088396
Abstract
Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to the costly data movement between the computation units and the memory units. Genome analysis and weather prediction are two examples of such applications. Recent FPGAs couple a reconfigurable fabric with high-bandwidth memory (HBM) to enable more efficient data movement and improve overall performance and energy efficiency. This trend is an example of a paradigm shift to near-memory computing. We leverage such an FPGA with high-bandwidth memory (HBM) for improving the pre-alignment filtering step of genome analysis and representative kernels from a weather prediction model. Our evaluation demonstrates large speedups and energy savings over a high-end IBM POWER9 system and a conventional FPGA board with DDR4 memory. We conclude that FPGA-based near-memory computing has the potential to alleviate the data movement bottleneck for modern data-intensive applications.
arXiv commentThis is an extended and updated version of a paper published in IEEE Micro, vol. 41, no. 4, pp. 39-48, 1 July-Aug. 2021
References in corpus (8)
- Minimap2: pairwise alignment for nucleotide sequences
- Nanopore Sequencing Technology and Tools for Genome Assembly: Computational Analysis of the Current State, Bottlenecks and Future Directions
- Technology dictates algorithms: Recent developments in read alignment
- GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies
- GateKeeper: A New Hardware Architecture for Accelerating Pre-Alignment in DNA Short Read Mapping
- Accelerating Genome Analysis: A Primer on an Ongoing Journey
- Apollo: A Sequencing-Technology-Independent, Scalable, and Accurate Assembly Polishing Algorithm
- Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture
Cited by in corpus (5)
- BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis
- SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
- Scrooge: A Fast and Memory-Frugal Genomic Sequence Aligner for CPUs, GPUs, and ASICs
- Automatic Creation of High-Bandwidth Memory Architectures from Domain-Specific Languages: The Case of Computational Fluid Dynamics
- Accelerating Genome Sequence Analysis via Efficient Hardware/Algorithm Co-Design