SHF: Small: CPU-GPU Collaborative Execution in Fusion Architectures

芯片设计的最新趋势是将通用中央处理单元(CPU)和图形处理单元(GPU)集成到单个微处理器芯片上。除了简单地将组件更紧密地放在一起的明显好处之外，这种集成为CPU和GPU提供了一个前所未有的合作机会，产生了一个性能远远超过其部件总和的系统。鉴于目前，CPU和GPU被委派了各自适合的不同任务，该项目探索了CPU和GPU处理和协作同一任务的新方法。这种协作与传统的并行处理有根本的不同，因为CPU和GPU拥有完全不同的体系结构。具体地说，CPU执行协助GPU任务的新颖元计算，反之亦然。这种创新方法为新兴的异类架构带来了新的机遇。该项目研究了CPU/GPU协作执行范例，以克服CPU和GPU计算任务的基本限制。通过对多个数据项执行一条指令，GPU实现了高计算吞吐量和高能效。如果一些数据项由于长时间的内存访问延迟而不可用，或者不同的数据项需要不同的操作，则其效率将严重下降。CPU/GPU协作利用CPU远远领先于GPU来预取数据，重新组织不同数据项所需的操作，从而大幅提高GPU的效率。相反，在CPU端，CPU/GPU协作利用GPU的并行处理能力来加速辅助计算，从而极大地丰富CPU程序。例如，局部性分析揭示了内存访问的性质，但在CPU上运行时需要很高的计算时间。通过GPU加速，可以与CPU程序同时执行局部性分析，并动态调整内存层次结构以提高CPU性能。这项研究跨越了软件和硬件两个层面。从软件的角度来看，该项目开发自动方法来生成用于协作执行的代码。从硬件的角度来看，未来的架构是为了促进更有效的CPU/GPU协作。自动化软件方法使现有和即将到来的微处理器能够更高效地运行，从而增加了它们的价值。性能提升和能源节约直接转化为增强的用户体验。

The most recent trend in chip design is to integrate general purpose central processing units (CPUs) with graphics processing units (GPUs) onto a single microprocessor chip. Looking beyond the obvious benefits of simply putting components closer together, such integration presents an unprecedented opportunity for the CPU and GPU to collaborate, yielding a system whose performance far exceeds the sum of its parts. Whereas, currently, the CPU and GPU are delegated different tasks that each is suited for, this project explores new ways for the CPU and GPU to tackle and collaborate on the same task. The collaboration is fundamentally different from conventional parallel processing, because the CPU and GPU have radically different architectures. In particular, the CPU performs novel meta-computation that assists a GPU task, or vice versa. This innovative approach uncovers new opportunities for emerging heterogeneous architectures. The project investigates CPU/GPU collaborative execution paradigms to overcome fundamental limitations of both CPU and GPU computing tasks. The GPU achieves high computational throughput and energy efficiency by executing a single instruction on many data items. Its efficiency is severely degraded if some data items are not available due to long memory access latency or different data items require different operations. The CPU/GPU collaboration leverages the CPU to run far ahead of the GPU to prefetch the data and reorganize the operations needed for different data items so as to drastically improve the GPU efficiency. Conversely, on the CPU side, the CPU/GPU collaboration leverages the GPU's parallel processing power to accelerate auxiliary computations that greatly enrich the CPU program. Locality analysis, for instance, reveals the nature of memory accesses but requires high computation time when running on a CPU. GPU acceleration makes it possible to perform locality analysis simultaneously with the CPU program and adapt the memory hierarchy on-the-fly to improve CPU performance. The research cuts through software and hardware layers. From the software perspective, the project develops automated approaches to generate code for collaborative execution. From the hardware perspective, future architectures are defined to facilitate more effective CPU/GPU collaboration. The automated software approach adds value to current and upcoming microprocessors by enabling them to run more efficiently. The performance improvement and energy savings translate directly into enhanced user experience.