一种利用辅助Warp加速GPU执行瓶颈的框架
A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps
摘要：
现代图形处理单元（GPU）被很好地提供以支持数千个线程的并发执行。不幸的是，执行过程中的不同瓶颈和异构的应用程序需求造成了核心资源利用率的不平衡。例如，当GPU被可用的片外内存带宽检查时，它的计算资源通常是压倒性的空闲，等待来自内存的数据到达。这项工作描述了核心辅助瓶颈加速（CABA）框架，采用空闲的片上资源，以减轻GPU执行中的不同瓶颈。CABA提供了灵活的机制来自动生成在GPU核心上执行的“辅助线程束”，以执行可以提高GPU性能和效率的特定任务。CABA使得能够使用空闲计算单元和流水线来缓解内存带宽瓶颈，例如，通过使用辅助扭曲来执行数据压缩以从存储器传送较少的数据。相反，可以采用相同的框架来处理GPU被可用计算单元校验的情况，在这种情况下，存储器流水线是空闲的，并且可以由CABA用来加速计算，例如，通过使用辅助线程束执行记忆化。我们提供了一个全面的设计和评估的CABA执行有效和灵活的数据压缩的GPU存储层次结构，以减轻内存带宽瓶颈。我们广泛的评估表明，CABA，当用于实现数据压缩，提供了41.7%的平均性能提高（高达2.6倍）在各种内存带宽敏感的GPGPU应用程序。
英文摘要：
Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the cores. For example, when a GPU is bottlenecked by the available off-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive. This work describes the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides flexible mechanisms to automatically generate "assist warps" that execute on GPU cores to perform specific tasks that can improve GPU performance and efficiency. CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps. We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.
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