skip to main content


Title: EXMA: A Genomics Accelerator for Exact-Matching
Genomics is the foundation of precision medicine, global food security and virus surveillance. Exact-match is one of the most essential operations widely used in almost every step of genomics such as alignment, assembly, annotation, and compression. Modern genomics adopts Ferragina-Manzini Index (FMIndex) augmenting space-efficient Burrows-Wheeler transform (BWT) with additional data structures to permit ultra-fast exact-match operations. However, FM-Index is notorious for its poor spatial locality and random memory access pattern. Prior works create GPU-, FPGA-, ASIC- and even process-in-memory (PIM)based accelerators to boost FM-Index search throughput. Though they achieve the state-of-the-art FM-Index search throughput, the same as all prior conventional accelerators, FM-Index PIMs process only one DNA symbol after each DRAM row activation, thereby suffering from poor memory bandwidth utilization. In this paper, we propose a hardware accelerator, EXMA, to enhance FM-Index search throughput. We first create a novel EXMA table with a multi-task-learning (MTL)-based index to process multiple DNA symbols with each DRAM row activation. We then build an accelerator to search over an EXMA table. We propose 2-stage scheduling to increase the cache hit rate of our accelerator. We introduce dynamic page policy to improve the row buffer hit rate of DRAM main memory. We also present CHAIN compression to reduce the data structure size of EXMA tables. Compared to state-of-the-art FM-Index PIMs, EXMA improves search throughput by 4.9 ×, and enhances search throughput per Watt by 4.8×.  more » « less
Award ID(s):
1908992 2105972
NSF-PAR ID:
10282764
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE International Symposium on High-Performance Computer Architecture
Page Range / eLocation ID:
399 to 411
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Genomics is the critical key to enabling precision medicine, ensuring global food security and enforcing wildlife conservation. The massive genomic data produced by various genome sequencing technologies presents a significant challenge for genome analysis. Because of errors from sequencing machines and genetic variations, approximate pattern matching (APM) is a must for practical genome analysis. Recent work proposes FPGA, ASIC and even process-in-memory-based accelerators to boost the APM throughput by accelerating dynamic-programming-based algorithms (e.g., Smith-Waterman). However, existing accelerators lack the efficient hardware acceleration for the exact pattern matching (EPM) that is an even more critical and essential function widely used in almost every step of genome analysis including assembly, alignment, annotation and compression. State-of-the-art genome analysis adopts the FM-Index that augments the space-efficient BWT with additional data structures permitting fast EPM operations. But the FM-Index is notorious for poor spatial locality and massive random memory accesses. In this paper, we propose a ReRAM-based process-in-memory architecture, FindeR, to enhance the FM-Index EPM search throughput in genomic sequences. We build a reliable and energy-efficient Hamming distance unit to accelerate the computing kernel of FM-Index search using commodity ReRAM chips without introducing extra CMOS logic. We further architect a full-fledged FM-Index search pipeline and improve its search throughput by lightweight scheduling on the NVDIMM. We also create a system library for programmers to invoke FindeR to perform EPMs in genome analysis. Compared to state-of-the-art accelerators, FindeR improves the FM-Index search throughput by 83% ~ 30K× and throughput per Watt by 3.5×~42.5K×. 
    more » « less
  2. In this paper, we propose ReDRAM, as a reconfigurable DRAM-based processing-in-memory (PIM) accelerator, which transforms current DRAM architecture to massively parallel computational units exploiting the high internal bandwidth of modern memory chips. ReDRAM uses the analog operation of DRAM sub-arrays and elevates it to implement a full set of 1- and 2-input bulk bit-wise operations (NOT, (N)AND, (N)OR, and even X(N)OR) between operands stored in the same bit-line, based on a new dual-row activation mechanism with a modest change to peripheral circuits such sense amplifiers. ReDRAM can be leveraged to greatly reduce energy consumption and latency of complex in-DRAM logic computations relying on state-of-the-art mechanisms based on triple-row activation, dual-contact cells, row initialization, NOR style, etc. The extensive circuit-architecture simulations show that ReDRAM achieves on average 54× and 7.1× higher throughput for performing bulk bit-wise operations compared with CPU and GPU, respectively. Besides, ReDRAM outperforms recent processing-in-DRAM platforms with up to 3.7× better performance. 
    more » « less
  3. Classified as a complex big data analytics problem, DNA short read alignment serves as a major sequential bottleneck to massive amounts of data generated by next-generation sequencing platforms. With Von-Neumann computing architectures struggling to address such computationally-expensive and memory-intensive task today, Processing-in-Memory (PIM) platforms are gaining growing interests. In this paper, an energy-efficient and parallel PIM accelerator (AlignS) is proposed to execute DNA short read alignment based on an optimized and hardware-friendly alignment algorithm. We first develop AlignS platform that harnesses SOT-MRAM as computational memory and transforms it to a fundamental processing unit for short read alignment. Accordingly, we present a novel, customized, highly parallel read alignment algorithm that only seeks the proposed simple and parallel in-memory operations (i.e. comparisons and additions). AlignS is then optimized through a new correlated data partitioning and mapping methodology that allows local storage and processing of DNA sequence to fully exploit the algorithm-level's parallelism, and to accelerate both exact and inexact matches. The device-to-architecture co-simulation results show that AlignS improves the short read alignment throughput per Watt per mm^2 by ~12X compared to the ASIC accelerator. Compared to recent FM-index-based ReRAM platform, AlignS achieves 1.6X higher throughput per Watt. 
    more » « less
  4. Technological advances in long read sequences have greatly facilitated the development of genomics. However, managing and analyzing the raw genomic data that outpaces Moore's Law requires extremely high computational efficiency. On the one hand, existing software solutions can take hundreds of CPU hours to complete human genome alignment. On the other hand, the recently proposed hardware platforms achieve low processing throughput with significant overhead. In this paper, we propose PARC, an Processing-in-Memory architecture for long read pairwise alignment leveraging emerging resistive CAM (content-addressable memory) to accelerate the bottleneck chaining step in DNA alignment. Chaining takes 2-tuple anchors as inputs and identifies a set of correlated anchors as potential alignment candidates. Unlike traditional main memory which organizes relational data structure in a linear address space, PARC stores tuples in two neighboring crossbar arrays with shared row decoder such that column-wise in-memory computational operations and row-wise memory accesses can be performed in-situ in a symmetric crossbar structure. Compared to both software tools and state-of-the-art accelerators, PARC shows significant improvement in alignment throughput and energy efficiency, thanks to the in-site computation capability and optimized data mapping. 
    more » « less
  5. This paper presents \textit{OFHE}, an electro-optical accelerator designed to process Discretized TFHE (DTFHE) operations, which encrypt multi-bit messages and support homomorphic multiplications, lookup table operations and full-domain functional bootstrappings. While DTFHE is more efficient and versatile than other fully homomorphic encryption schemes, it requires 32-, 64-, and 128-bit polynomial multiplications, which can be time-consuming. Existing TFHE accelerators are not easily upgradable to support DTFHE operations due to limited datapaths, a lack of datapath bit-width reconfigurability, and power inefficiencies when processing FFT and inverse FFT (IFFT) kernels. Compared to prior TFHE accelerators, OFHE addresses these challenges by improving the DTFHE operation latency by 8.7\%, the DTFHE operation throughput by $57\%$, and the DTFHE operation throughput per Watt by $94\%$. 
    more » « less