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）与图形处理单元（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提供了前所未有的合作机会，从而产生了一个性能远远超过其部件之和的系统。目前，CPU和GPU被委派了各自适合的不同任务，而这个项目探索了CPU和GPU处理和协作同一任务的新方法。这种协作从根本上不同于传统的并行处理，因为CPU和GPU具有完全不同的架构。特别地，CPU执行辅助GPU任务的新颖元计算，反之亦然。这种创新方法为新兴的异构体系结构带来了新的机遇。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。GPU通过对许多数据项执行单个指令来实现高计算吞吐量和能量效率。如果由于长的存储器访问延迟或不同的数据项需要不同的操作而导致某些数据项不可用，则其效率会严重降低。CPU/GPU协作利用CPU远远领先于GPU运行，以预取数据并重新组织不同数据项所需的操作，从而大幅提高GPU效率。相反，在CPU方面，CPU/GPU协作利用GPU的并行处理能力来加速辅助计算，从而极大地丰富了CPU程序。例如，局部性分析揭示了内存访问的本质，但在CPU上运行时需要很长的计算时间。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。这项研究贯穿了软件和硬件两个层面。从软件的角度来看，该项目开发了自动化的方法来生成协作执行的代码。从硬件的角度来看，未来的架构被定义为促进更有效的CPU/GPU协作。自动化软件方法通过使当前和即将推出的微处理器能够更有效地运行来增加其价值。性能提升和节能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.