Timescale Functions for Parallel Memory Allocation
Memory allocation is increasingly important to parallel performance, yet it is challenging because a program has data of many sizes, and the demand differs from thread to thread. Modern allocators use highly tuned heuristics but do not provide uniformly good performance when the level of concurrency increases from a few threads to hundreds of threads.
This paper presents a new timescale theory to model the memory demand in real time. Using the new theory, an allocator can adjust its synchronization frequency using a single parameter called allocations per fetch (apf ). The paper presents the timescale theory, the design and implementation of APF tuning in an existing allocator, and evaluation of the effect on program speed and memory efficiency. APF tuning improves the throughput of MongoDB by 55%, reduces the tail latency of a Web server by over 60%, and increases the speed of a selection of synthetic benchmarks by up to 24× while using the same amount of memory.
Sun 23 JunDisplayed time zone: Tijuana, Baja California change
11:20 - 12:35
|Exploration of Memory Hybridization for RDD Caching in Spark
|Learning When to Garbage Collect with Random Forests
|Timescale Functions for Parallel Memory Allocation