Collaborative Research: PPoSS: LARGE: Panorama: Integrated Rack-Scale Acceleration for Computational Pangenomics

合作研究：PPoSS：大型：全景：计算泛基因组学的集成机架规模加速

• 批准号: 2118628
• 负责人: Michael Taylor
• 金额: $ 111.16万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118628&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; LARGE; Panorama

Semiconductor technology scaling is slowing, and as a consequence, computer systems must increasingly rely on a heterogeneous mix of general-purpose and specialized computing engines. At the same time, computer users are tackling new problems with massive datasets that must be manipulated in irregular and rapidly changing ways while maintaining strict privacy guarantees. Efficiently supporting big, sparse, dynamic, and private data to solve large complex problems on heterogeneous systems is one of the grand challenges in software/hardware systems research. To address this grand challenge, the Panorama project is exploring integrated rack-scale acceleration for computational pangenomics. Integrated rack-scale acceleration refers to an emerging computer-systems paradigm that uses tens of tightly integrated computing nodes, each of which includes a mix of general-purpose processors and specialized accelerators interconnected with a special-purpose network. Computational pangenomics refers to a recent trend towards representing genomes, the genetic material of an organism, not as a single linear sequence of DNA base pairs but instead as an intricate network of sequences that efficiently represents the relationships between many individuals' genomes at once. Computational pangenomics naturally captures the trend towards big, sparse, dynamic, and private data and is thus a perfect application domain to explore heterogeneous software/hardware systems research. The project's novelties are: a truly cross-stack approach spanning applications, programming languages, compilers, architecture, security, and privacy including use of a one-of-a-kind Panorama prototype system; new hardware techniques to accelerate domain-specific computing and to unify heterogeneous systems; new software techniques to let programmers harness the performance advantages of heterogeneous systems; and new software/hardware techniques to make such heterogeneous systems more secure. The project's impacts are: to specifically enable computational biologists to better see the "genetic dark matter" of population-wide genomics which has been to date hidden, opening up new scientific discoveries; and to more generally enable future computer users to more easily take advantage of heterogeneous computer systems to solve large and complex problems. This project is also pursuing two broader impact initiatives. The first is an ambitious yet concrete initiative to increase participation of under-represented minority students in computer science by developing a low-level computer-systems module for a new four-week summer program targeting rising sophomores. The second involves specific plans to grow the open-source software/hardware ecosystem in the computational-biology and computer-systems communities.The Panorama project includes a highly interdisciplinary team of researchers across four focus areas: applications (computational biology), programming languages & compilers, computer architecture, and security & privacy. The team is taking a holistic software/hardware co-design approach to explore five tightly interconnected research thrusts. The first three thrusts are structured from top-down across the computing stack. Thrust 1 investigates new computational pangenomics data structures and algorithms and will develop PanoBench, a new benchmark suite suitable for driving the remaining thrusts. Thrust 2 investigates new programming-language and compiler techniques. Thrust 3 investigates new computer architectures with support for a whole-rack manycore with 1M+ cores and a partitioned global address space, unified array-based accelerators, and application-specific accelerator chiplets for computational pangenomics. The final two thrusts cut across both software and hardware. Thrust 4 investigates new security and privacy techniques including scalable secure computation on heterogeneous rack-scale systems, secure rack-scale resource management with auto-tuning, and differential privacy and homomorphic encryption for pangenomics. Thrust 5 involves holistically evaluating the research ideas in the other thrusts through the use of a one-of-a-kind Panorama prototype system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

半导体技术的扩展正在放缓，因此，计算机系统必须越来越多地依赖于通用和专用计算引擎的异构混合。与此同时，计算机用户正在处理大量数据集的新问题，这些数据集必须以不规则和快速变化的方式进行操作，同时保持严格的隐私保证。有效地支持大的，稀疏的，动态的，和私有的数据来解决异构系统上的大型复杂问题是软件/硬件系统研究的重大挑战之一。为了应对这一巨大挑战，Panorama项目正在探索用于计算泛基因组学的集成机架级加速。集成机架级加速是指使用数十个紧密集成的计算节点的新兴计算机系统范例，其中每个节点包括与专用网络互连的通用处理器和专用加速器的混合。计算泛基因组学（Computational pangenomics）指的是最近的一种趋势，即将基因组（生物体的遗传物质）表示为一个复杂的序列网络，而不是DNA碱基对的单一线性序列，它可以同时有效地表示许多个体基因组之间的关系。计算泛基因组学自然地捕捉到了大数据、稀疏数据、动态数据和私有数据的趋势，因此是探索异构软件/硬件系统研究的完美应用领域。该项目的创新之处是：一个真正的跨堆栈的方法跨越应用程序，编程语言，编译器，架构，安全性和隐私，包括使用一个独一无二的全景原型系统;新的硬件技术，以加速特定领域的计算和统一异构系统;新的软件技术，让程序员利用异构系统的性能优势;和新的软件/硬件技术，使这种异构系统更安全。该项目的影响是：特别是使计算生物学家能够更好地看到迄今为止一直被隐藏的整个人口基因组学的“遗传暗物质”，从而开辟新的科学发现;更普遍地使未来的计算机用户能够更容易地利用异构计算机系统来解决大型和复杂的问题。该项目还在实施两项影响更广泛的举措。第一个是一个雄心勃勃但具体的倡议，通过开发一个低层次的计算机系统模块，为一个新的为期四周的夏季计划，针对上升的少数民族学生，以增加在计算机科学的代表性不足的学生的参与。第二个是在计算生物学和计算机系统社区中发展开源软件/硬件生态系统的具体计划。Panorama项目包括一个高度跨学科的研究人员团队，他们横跨四个重点领域：应用程序（计算生物学）、编程语言编译器、计算机体系结构和安全隐私。该团队正在采取整体的软件/硬件协同设计方法，探索五个紧密相连的研究方向。前三个重点是从上到下贯穿计算堆栈的结构。Thrust 1研究了新的计算泛基因组学数据结构和算法，并将开发PanoBench，这是一个适合推动剩余推力的新基准套件。推力2研究新的编程语言和编译器技术。Thrust 3研究了新的计算机架构，支持具有1 M+核心的全机架众核和分区的全局地址空间，统一的基于阵列的加速器，以及用于计算泛基因组学的特定于应用的加速器小芯片。最后两个突破点跨越了软件和硬件。Thrust 4研究了新的安全和隐私技术，包括异构机架规模系统上的可扩展安全计算，具有自动调整的安全机架规模资源管理，以及泛基因组学的差分隐私和同态加密。第5个重点是通过使用独一无二的全景原型系统对其他重点的研究思想进行全面评估。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

ProcessorFuzz: Processor Fuzzing with Control and Status Registers Guidance

• DOI: 10.1109/host55118.2023.10133714
• 发表时间: 2023-05
• 期刊: 2023 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
• 作者: Sadullah Canakci;Chathura Rajapaksha;Leila Delshadtehrani;A. Nataraja;Michael B. Taylor;Manuel Egele;Ajay Joshi

Beyond Static Parallel Loops: Supporting Dynamic Task Parallelism on Manycore Architectures with Software-Managed Scratchpad Memories

• DOI: 10.1145/3582016.3582020
• 发表时间: 2023-03
• 期刊: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
• 作者: Lin Cheng;Max Ruttenberg;Dai Cheol Jung;D. Richmond;M. Taylor;M. Oskin;C. Batten