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GCMA: Guaranteed Contiguous Memory Allocator

机译:GCMA:保证的连续内存分配器

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The importance of physically contiguous memory has increased in modern computing environments, including both low- and high-end systems. Existing physically contiguous memory allocators generally have critical limitations. For example, the most commonly adopted solution, the memory reservation technique, wastes a significant amount of memory space. Scatter/Gather direct memory access (DMA) and input-output memory management units (IOMMUs) avoid this problem by utilizing additional hardware for address space virtualization. However, additional hardware means an increase in costs and power consumption, which is especially disadvantageous for small systems and they do not provide real contiguous memory. Linux Contiguous Memory Allocator (CMA) aims to provide both contiguous memory allocation and to maximize memory utilization based on page migration, but they suffer from unpredictably long latency and a high probability of allocation failure. Therefore, we introduce a new solution to this problem, the guaranteed contiguous memory allocator (GCMA). This guarantees efficient memory space utilization, short latency, and successful allocation. The GCMA uses a reservation scheme and increases memory utilization by sharing the memory with immediately discardable data. Our evaluation of a GCMA on a Raspberry Pi 2 finds a latency that is 15-130 times lower compared to a CMA, and a latency that is up to 10 times lower when taking a photo. Using a large working set in a memory-fragmented high-end system, the GCMA is able to produce a 2.27x speedup.
机译:在现代计算环境(包括低端和高端系统)中,物理连续内存的重要性已经提高。现有的物理上连续的内存分配器通常具有关键限制。例如,最常用的解决方案,即内存保留技术,浪费了大量的内存空间。分散/聚集直接内存访问(DMA)和输入输出内存管理单元(IOMMU)通过利用其他硬件进行地址空间虚拟化来避免此问题。但是,额外的硬件意味着成本和功耗的增加,这对于小型系统尤其不利,并且它们不提供真正的连续内存。 Linux连续内存分配器(CMA)旨在提供连续的内存分配并基于页面迁移来最大化内存利用率,但是它们遭受意想不到的长等待时间和很高的分配失败可能性。因此,我们针对此问题引入了一种新的解决方案,即保证连续内存分配器(GCMA)。这保证了有效的内存空间利用率,较短的延迟和成功的分配。 GCMA使用预留方案,并通过与立即丢弃的数据共享内存来提高内存利用率。我们对Raspberry Pi 2上的GCMA的评估发现,与CMA相比,其延迟降低了15-130倍,而在拍照时的延迟则降低了10倍。在内存分散的高端系统中使用大型工作集,GCMA能够产生2.27倍的加速比。

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