EAGER: Exploring the Foundations of High-Level Programming Models for GPUs

现代图形处理器单元（GPU）的性能在几年前还被归类为超级计算机级别。随着将GPU核心集成到异构多核处理器中的趋势，GPU正在成为主流处理器未来性能增长的重要来源。 不幸的是，GPU是出了名的难以编程。 用于GPU编程的标准语言暴露了低级架构细节，例如显式内存层次结构，程序员必须利用这些细节来最大化性能。此外，GPU硬件发展迅速，一代又一代的硬件具有不同的性能特征，这意味着应用程序代码必须不断修改以实现最佳性能。为了使GPU、加速器和异构架构的强大功能得到更广泛的应用，编程模型需要从硬件线程和内存模型的细节中提升抽象层次。 这个EAGER项目确定了通用的抽象和实现技术，可以为加速器和异构体系结构上支持不规则计算提供基础。 该项目的一个重点是设计一个核心的异质性演算，可以模拟在异构系统上运行的程序，如CPU/GPU组合，并为未来的工作在语义和GPU并行编程语言的实现奠定基础。该项目的结果将GPU的适用性扩展到更广泛的计算问题，反过来，通过支持比现有语言更易于使用的GPU高级编程模型，将有助于使GPU对更广泛的用户社区有用。

Modern Graphics-Processor Units (GPUs) are capable of performance that, just a few years ago, would have been classified as supercomputer-level. With the trend of integrating GPU cores into heterogeneous multicore processors, GPUs are becoming an important source of future performance growth in mainstream processors. Unfortunately, GPUs are notoriously hard to program. The standard languages for programming GPUs expose low-level architectural details, such as an explicit memory hierarchy, which the programmer must exploit to maximize performance. Furthermore, GPU hardware evolves rapidly, with successive generations of hardware having different performance characteristics, which means that application code must be constantly revised to achieve optimal performance.To make the power of GPUs, accelerators, and heterogeneous architectures more widely applicable, programming models need to lift the level of abstraction away from the details of the hardware threading and memory models. This EAGER project identifies common abstractions and implementation techniques that can provide a foundation for supporting irregular computations on accelerator and heterogeneous architectures. A key focus of the project is the design of a core calculus of heterogeneity that can model programs that run on heterogeneous systems, such as CPU/GPU combinations, and provide a foundation for future work in both the semantics and implementation of parallel programming languages for GPUs. The results of the project broaden the applicability of GPUs to a wider range of computational problems and, in turn, will help make GPUs useful to a broader community of users by supporting higher-level programming models for GPUs that are easier to use than existing languages.