Performance for All: Broadening the Scope of Parallelism and Specialization

能源效率是现代计算机体系结构面临的关键挑战。对更高性能的需求将继续其历史性的增长，但支持数十年性能和效率改进的技术扩展趋势现在正在停滞不前。为了满足用户需求和持续的性能扩展，架构需要新的方法来降低每次操作的能耗。并行化（同时运行许多操作）和专门化（为给定的应用程序域或模式集定制电路和数据移动）是提高效率的两种众所周知的互补技术。少数应用领域已经激发了对专门架构的大量投资（例如，Google的TPU），主要是由于其领域的巨大经济价值，以及简化硬件设计的“令人尴尬”的数据并行量。不幸的是，对于绝大多数应用程序来说，制造定制硬件的固定成本令人望而却步，而且通常它们具有更复杂的不规则并行形式，与现代多核，GPU和其他加速器不匹配。这些应用程序目前没有未来性能提升的途径，限制了可以解决的问题的大小和范围。该研究计划采取整体方法，通过共同设计编程模型，多核，可重新配置加速器，编译器和语言，为这些服务不足的应用程序的沉重尾部提高所有人的性能。我们将解决应用领域的计算瓶颈，如统计推断，图形分析，电子CAD，数学优化等。 通过该计划，PI将在加拿大内外的技术行业所追求的计算机体系结构和系统领域培训高素质人员（HQP）。具体技能包括性能分析，编译器优化，系统仿真和原型设计，以及硬件-软件协同设计。PI将在HQP招聘和培训环境中解决公平、多样性和包容性问题。PI将广泛宣传并积极招聘，以吸引多元化的申请人。PI将在培训期间定期强调EDI原则，并将通过将代表性不足的少数民族与计算机架构中的女性或人工智能中的黑人等群体联系起来来提高HQP的形象。

Energy efficiency is the critical challenge facing modern computer architectures. Demand for higher performance will continue its historic growth, yet the technology scaling trends that backed decades of performance and efficiency improvements are now flat-lining. To meet user demand and continue performance scaling, architectures require new ways to reduce energy per operation. Parallelism (running many operations simultaneously) and specialization (customizing circuitry and data movement for a given application domain or set of patterns) are two well-known and complementary techniques to improve efficiency. A small minority of application domains have inspired significant investment into specialized architectures (e.g., Google's TPU), largely enabled by the enormous economic value of their domains, and "embarrassing" amounts of data parallelism which simplify hardware design. Unfortunately, for the vast majority of applications, the fixed cost of fabricating custom hardware is prohibitive, and in general they have more complex forms of irregular parallelism that are a poor match to modern multicores, GPUs, and other accelerators. These applications currently have no pathway to future performance gains, limiting the size and scope of problems that can be solved. This research program takes a holistic approach to improve performance for all by co-designing programming models, multicores, reconfigurable accelerators, compilers, and languges for the heavy tail of these underserved applications. We will solve compute bottlenecks in application domains such as statistical inference, graph analytics, electronic CAD, mathematical optimization, and more. Through the program, the PI will train highly-qualified personnel (HQPs) in the areas of computer architecture and systems which are sought-after in the technology industries within and outside Canada. Particular skills include performance analysis, compiler optimization, system simulation and prototyping, and hardware-software co-design. The PI will address equity, diversity, and inclusion (EDI) in HQP recruitment and the training environment. The PI will advertise broadly and recruit proactively to attract a diverse applicant pool. The PI will routinely emphasize EDI principles during training, and will raise HQP profiles by connecting those from under-represented minorities to groups such as Women in Computer Architecture or Black in AI.