Multiple CPUs and GPUs are integrated on the same chip to share memory, and access requests between cores are interfering with each other. Memory requests from the GPU seriously interfere with the CPU memory access performance. Requests between multiple CPUs are intertwined when accessing memory, and its performance is greatly affected. The difference in access latency between GPU cores increases the average latency of memory accesses. In order to solve the problems encountered in the shared memory of heterogeneous multi-core systems, we propose a step-by-step memory scheduling strategy, which improve the system performance. The step-by-step memory scheduling strategy first creates a new memory request queue based on the request source and isolates the CPU requests from the GPU requests when the memory controller receives the memory request, thereby preventing the GPU request from interfering with the CPU request. Then, for the CPU request queue, a dynamic bank partitioning strategy is implemented, which dynamically maps it to different bank sets according to different memory characteristics of the application, and eliminates memory request interference of multiple CPU applications without affecting bank-level parallelism. Finally, for the GPU request queue, the criticality is introduced to measure the difference of the memory access latency between the cores. Based on the first ready-first come first served strategy, we implemented criticality-aware memory scheduling to balance the locality and criticality of application access.

中文翻译：

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一种消除异构系统内存访问干扰的内存调度策略

多个 CPU 和 GPU 集成在同一芯片上共享内存，内核之间的访问请求相互干扰。来自 GPU 的内存请求严重干扰 CPU 内存访问性能。多个CPU之间的请求在访问内存时是交织在一起的，其性能受到很大的影响。GPU 内核之间访问延迟的差异增加了内存访问的平均延迟。为了解决异构多核系统在共享内存中遇到的问题，我们提出了一种逐步的内存调度策略，从而提高了系统性能。分步式内存调度策略首先根据请求源创建一个新的内存请求队列，并在内存控制器收到内存请求时将CPU请求与GPU请求隔离，从而防止 GPU 请求干扰 CPU 请求。然后，针对CPU请求队列，实现了动态bank分区策略，根据应用的不同内存特性动态映射到不同的bank集合，在不影响bank级并行度的情况下，消除多个CPU应用的内存请求干扰。最后，对于GPU请求队列，引入了criticality来衡量内核之间内存访问延迟的差异。基于先到先得的策略，我们实现了关键性感知内存调度，以平衡应用程序访问的局部性和关键性。根据应用的不同内存特性动态映射到不同的bank集合，在不影响bank级并行度的情况下，消除多个CPU应用的内存请求干扰。最后，对于GPU请求队列，引入了criticality来衡量内核之间内存访问延迟的差异。基于先到先得的策略，我们实现了关键性感知内存调度，以平衡应用程序访问的局部性和关键性。根据应用的不同内存特性动态映射到不同的bank集合，在不影响bank级并行度的情况下，消除多个CPU应用的内存请求干扰。最后，对于GPU请求队列，引入了criticality来衡量内核之间内存访问延迟的差异。基于先到先得的策略，我们实现了关键性感知内存调度，以平衡应用程序访问的局部性和关键性。引入临界性来衡量内核之间的内存访问延迟差异。基于先到先得的策略，我们实现了关键性感知内存调度，以平衡应用程序访问的局部性和关键性。引入关键性来衡量内核之间的内存访问延迟差异。基于先到先得的策略，我们实现了关键性感知内存调度，以平衡应用程序访问的局部性和关键性。