FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces

Dataflow acceleration of Smith-Waterman with Traceback for high throughput Next Generation Sequencing

Smith-Waterman algorithm is widely adopted bymost popular DNA sequence aligners. The inherent algorithmcomputational intensity and the vast amount of NGS input datait operates on, create a bottleneck in genomic analysis flows forshort-read alignment. FPGA architectures have been extensivelyleveraged to alleviate the problem, each one adopting a differentapproach. In existing solutions, effective co-design of the NGSshort-read alignment still remains an open issue, mainly due tonarrow view on real integration aspects, such as system widecommunication and accelerator call overheads. In this paper, wepropose a dataflow architecture for Smith-Waterman Matrix-filland Traceback alignment stages, to perform short-read alignmenton NGS data. The architectural decision of moving both stages onchip extinguishes the communication overhead, and coupled withradical software restructuring, allows for efficient integration intowidely-used Bowtie2 aligner. This approach delivers×18 speedupover the respective Bowtie2 standalone components, while our co-designed Bowtie2 demonstrates a 35% boost in performance

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to appreciate the impact of System-on-Chip (SoC) resource contention, OS overheads, and programming-stack inefficiencies on overall performance/energy-efficiency. To address this challenge, we present Gemmini, an open-source*, full-stack DNN accelerator generator. Gemmini generates a wide design-space of efficient ASIC accelerators from a flexible architectural template, together with flexible programming stacks and full SoCs with shared resources that capture system-level effects. Gemmini-generated accelerators have also been fabricated, delivering up to three orders-of-magnitude speedups over high-performance CPUs on various DNN benchmarks. * https://github.com/ucb-bar/gemminiComment: To appear at the 58th IEEE/ACM Design Automation Conference (DAC), December 2021, San Francisco, CA, US

caos cad as an adaptive open platform service for high performance reconfigurable systems

The increasing demand for computing power in fields such as genomics, image processing and machine learning is pushing towards hardware specialization and heterogeneous systems in order to keep up with the required performance level at sustainable power consumption. Among the available solutions, Field Programmable Gate Arrays (FPGAs), thanks to their advancements, currently represent a very promising candidate, offering a compelling trade-off between efficiency and flexibility. Despite the potential benefits of reconfigurable hardware, one of the main limiting factor to the widespread adoption of FPGAs is complexity in programmability, as well as the effort required to port software solutions to efficient hardware-software implementations. In this chapter, we present CAD as an Adaptive Open-platform Service (CAOS), a platform to guide the application developer in the implementation of efficient hardware-software solutions for high performance reconfigurable systems. The platform assists the designer from the high-level analysis of the code, towards the optimization and implementation of the functionalities to be accelerated on the reconfigurable nodes. Finally, CAOS is designed to facilitate the integration of external contributions and to foster research on Computer Aided Design (CAD) tools for accelerating software applications on FPGA-based systems

Methodology for complex dataflow application development

This thesis addresses problems inherent to the development of complex applications for reconfig- urable systems. Many projects fail to complete or take much longer than originally estimated by relying on traditional iterative software development processes typically used with conventional computers. Even though designer productivity can be increased by abstract programming and execution models, e.g., dataflow, development methodologies considering the specific properties of reconfigurable systems do not exist. The first contribution of this thesis is a design methodology to facilitate systematic develop- ment of complex applications using reconfigurable hardware in the context of High-Performance Computing (HPC). The proposed methodology is built upon a careful analysis of the original application, a software model of the intended hardware system, an analytical prediction of performance and on-chip area usage, and an iterative architectural refinement to resolve identi- fied bottlenecks before writing a single line of code targeting the reconfigurable hardware. It is successfully validated using two real applications and both achieve state-of-the-art performance. The second contribution extends this methodology to provide portability between devices in two steps. First, additional tool support for contemporary multi-die Field-Programmable Gate Arrays (FPGAs) is developed. An algorithm to automatically map logical memories to hetero- geneous physical memories with special attention to die boundaries is proposed. As a result, only the proposed algorithm managed to successfully place and route all designs used in the evaluation while the second-best algorithm failed on one third of all large applications. Second, best practices for performance portability between different FPGA devices are collected and evaluated on a financial use case, showing efficient resource usage on five different platforms. The third contribution applies the extended methodology to a real, highly demanding emerging application from the radiotherapy domain. A Monte-Carlo based simulation of dose accumu- lation in human tissue is accelerated using the proposed methodology to meet the real time requirements of adaptive radiotherapy.Open Acces

A framework for automatically generating optimized digital designs from C-language loops

Reconfigurable computing has the potential for providing significant performance increases to a number of computing applications. However, realizing these benefits requires digital design experience and knowledge of hardware description languages (HDLs). While a number of tools have focused on translation of high-level languages (HLLs) to HDLs, the tools do not always create optimized digital designs that are competitive with hand-coded solutions. This work describes an automatic optimization in the C-to-HDL transformation that reorganizes operations between pipeline stages in order to reduce critical path lengths. The effects of this optimization are examined on the MD5, SHA-1, and Smith-Waterman algorithms. Results show that the optimization results in performance gains of 13%-37% and that the automatically-generated implementations perform comparably to hand-coded implementations

A framework for automatically generating optimized digital designs from C-language loops

Reconfigurable computing has the potential for providing significant performance increases to a number of computing applications. However, realizing these benefits requires digital design experience and knowledge of hardware description languages (HDLs). While a number of tools have focused on translation of high-level languages (HLLs) to HDLs, the tools do not always create optimized digital designs that are competitive with hand-coded solutions. This work describes an automatic optimization in the C-to-HDL transformation that reorganizes operations between pipeline stages in order to reduce critical path lengths. The effects of this optimization are examined on the MD5, SHA-1, and Smith-Waterman algorithms. Results show that the optimization results in performance gains of 13%-37% and that the automatically-generated implementations perform comparably to hand-coded implementations

Throughput-optimal systolic arrays from recurrence equations

Many compute-bound software kernels have seen order-of-magnitude speedups on special-purpose accelerators built on specialized architectures such as field-programmable gate arrays (FPGAs). These architectures are particularly good at implementing dynamic programming algorithms that can be expressed as systems of recurrence equations, which in turn can be realized as systolic array designs. To efficiently find good realizations of an algorithm for a given hardware platform, we pursue software tools that can search the space of possible parallel array designs to optimize various design criteria. Most existing design tools in this area produce a design that is latency-space optimal. However, we instead wish to target applications that operate on a large collection of small inputs, e.g. a database of biological sequences. For such applications, overall throughput rather than latency per input is the most important measure of performance. In this work, we introduce a new procedure to optimize throughput of a systolic array subject to resource constraints, in this case the area and bandwidth constraints of an FPGA device. We show that the throughput of an array is dependent on the maximum number of lattice points executed by any processor in the array, which to a close approximation is determined solely by the array’s projection vector. We describe a bounded search process to find throughput-optimal projection vectors and a tool to perform automated design space exploration, discovering a range of array designs that are optimal for inputs of different sizes. We apply our techniques to the Nussinov RNA folding algorithm to generate multiple mappings of this algorithm into systolic arrays. By combining our library of designs with run-time reconfiguration of an FPGA device to dynamically switch among them, we predict significant speedup over a single, latency-space optimal array