Novel domain-specific languages and compiler optimization methods for computational biology

计算生物学的新颖的特定领域语言和编译器优化方法

• 批准号: RGPIN-2019-04973
• 负责人: Numanagic, Ibrahim
• 金额: $ 2.04万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737985
• 关键词: Novel; domain; specific; languages; compiler

Motivation: The vast scale of data generated by next-generation sequencing (NGS) experiments necessitates the development of efficient computational methods, as the computational aspect is currently the biggest bottleneck of NGS pipelines. However, many promising methods cannot handle the scale of current and emerging NGS technologies and are too hard to use and replicate. Root cause of this problem lies in widely used general-purpose development environments that cannot efficiently express and optimize biological data workflows. Users are forced to use either high-level but slow languages such as Python, or low-level languages such as C that produce efficient tools but at a significant time and maintainability costs. Approach: A domain-specific language (DSL) and associated suite of compiler optimization techniques specifically tailored for biological and sequencing data would provide flexibility, simplicity and modularity for experimenting with new computational algorithms, while generating high-performance code and making the programs portable from resource-constrained architectures to the biggest supercomputers. Objectives: We propose a novel DSL and associated compiler named Seq that enables rapid and easy development of high-performance sequencing pipelines. To achieve this, we will: (i)design a programming language and a compiler that allows ease of development of high-level languages such as Python, while providing raw performance of low-level languages such as C (Objective 1); (ii)explore data access patterns in genomic workflows and devise methods that can exploit these patterns at the compiler level for automatic low-level optimizations across various computational environments, such as multicore CPUs, GPUs and handheld devices (Objective 2); and (iii)provide means to easily integrate Seq into popular bioinformatics and scientific environments and develop a curated library of algorithmic primitives for NGS data (Objective 3). The short-term goal is to develop a DSL that can efficiently handle various kinds of NGS data on common architectures. The long-term goal is to build a comprehensive and widely used infrastructure that allows rapid and easy method development for biological data. As computational biology HQP are in high demand in Canada, one of the key goals of this proposal is to train HQP over the course of five years. Impact: We envision our DSL to significantly boost Canadian genomics and health research by enabling researchers to express their ideas in a more natural way and by allowing them to use the best algorithmic methods for the job. Furthermore, we expect our DSL to aid large-scale scientific Canadian health projects by providing huge time and cost savings. We also anticipate Seq to become a key building block in the wide specter of widely used bioinformatics tools. Finally, we expect that HQP trained by this program will contribute to the Canadian knowledge-based economy.

动机：下一代测序（NGS）实验产生的海量数据需要开发高效的计算方法，因为计算方面是目前NGS流程的最大瓶颈。然而，许多有前景的方法无法应对当前和新兴 NGS 技术的规模，并且难以使用和复制。这个问题的根本原因在于广泛使用的通用开发环境无法有效表达和优化生物数据工作流程。用户被迫使用高级但速度较慢的语言（例如 Python），或者使用低级语言（例如 C），这些语言可以生成高效的工具，但需要花费大量的时间和可维护性成本。 方法：专门针对生物和测序数据定制的领域特定语言（DSL）和相关的编译器优化技术套件将为试验新的计算算法提供灵活性、简单性和模块化，同时生成高性能代码并使程序从资源受限的体系结构移植到最大的超级计算机。目标：我们提出了一种新颖的 DSL 和名为 Seq 的相关编译器，可以快速轻松地开发高性能测序管道。为了实现这一目标，我们将： (i) 设计一种编程语言和编译器，使其能够轻松开发 Python 等高级语言，同时提供 C 等低级语言的原始性能（目标 1）； (ii) 探索基因组工作流程中的数据访问模式，并设计可以在编译器级别利用这些模式的方法，以跨各种计算环境（例如多核 CPU、GPU 和手持设备）进行自动低级优化（目标 2）； (iii) 提供将 Seq 轻松集成到流行的生物信息学和科学环境中的方法，并为 NGS 数据开发一个精选的算法原语库（目标 3）。 短期目标是开发一种能够在通用架构上高效处理各种 NGS 数据的 DSL。长期目标是建立一个全面且广泛使用的基础设施，以便快速、轻松地开发生物数据的方法。由于加拿大对计算生物学 HQP 的需求量很大，因此该提案的主要目标之一是在五年内培训 HQP。影响：我们设想我们的 DSL 能够使研究人员以更自然的方式表达他们的想法，并允许他们使用最好的算法方法来完成工作，从而显着促进加拿大基因组学和健康研究。此外，我们希望我们的 DSL 能够节省大量时间和成本，从而为加拿大大型科学健康项目提供帮助。我们还预计 Seq 将成为广泛使用的生物信息学工具的关键组成部分。最后，我们期望通过该项目培训的HQP将为加拿大知识经济做出贡献。