학술논문

Reducing Minor Page Fault Overheads through Enhanced Page Walker
Document Type
Working Paper
Source
Subject
Computer Science - Hardware Architecture
Language
Abstract
Application virtual memory footprints are growing rapidly in all systems from servers down to smartphones. To address this growing demand, system integrators are incorporating ever larger amounts of main memory, warranting rethinking of memory management. In current systems, applications produce page fault exceptions whenever they access virtual memory regions which are not backed by a physical page. As application memory footprints grow, they induce more and more minor faults. Handling of each minor fault can take few 1000's of CPU-cycles and blocks the application till OS finds a free physical frame. These page faults can be detrimental to the performance, when their frequency of occurrence is high and spread across application run-time. Specifically, lazy allocation induced minor page faults are increasingly impacting application performance. Our evaluation of several workloads indicates an overhead due to minor faults as high as 29% of execution time. In this paper, we propose to mitigate this problem through a hardware, software co-design approach. Specifically we first propose to parallelize portions of the kernel page allocation to run ahead of fault time in a separate thread. Then we propose the Minor Fault Offload Engine(MFOE), a per-core HW accelerator for minor fault handling. MFOE is equipped with pre-allocated page frame table that it uses to service a page fault. On a page fault, MFOE picks a pre-allocated page frame from this table, makes an entry for it in the TLB, and updates the page table entry to satisfy the page fault. The pre-allocation frame tables are periodically refreshed by a background kernel thread, which also updates the kernel memory management data-structures. We evaluate this system in the gem5 architectural simulator with a modified Linux kernel. Our results show that MFOE improves the average critical-path fault handling latency by 33x.
Comment: To appear in ACM Transactions on Architecture and Code Optimization (TACO)