Memory management

About: Memory management is a research topic. Over the lifetime, 16743 publications have been published within this topic receiving 312028 citations. The topic is also known as: memory allocation.

NV-Heaps: making persistent objects fast and safe with next-generation, non-volatile memories

Abstract: Persistent, user-defined objects present an attractive abstraction for working with non-volatile program state. However, the slow speed of persistent storage (i.e., disk) has restricted their design and limited their performance. Fast, byte-addressable, non-volatile technologies, such as phase change memory, will remove this constraint and allow programmers to build high-performance, persistent data structures in non-volatile storage that is almost as fast as DRAM. Creating these data structures requires a system that is lightweight enough to expose the performance of the underlying memories but also ensures safety in the presence of application and system failures by avoiding familiar bugs such as dangling pointers, multiple free()s, and locking errors. In addition, the system must prevent new types of hard-to-find pointer safety bugs that only arise with persistent objects. These bugs are especially dangerous since any corruption they cause will be permanent.We have implemented a lightweight, high-performance persistent object system called NV-heaps that provides transactional semantics while preventing these errors and providing a model for persistence that is easy to use and reason about. We implement search trees, hash tables, sparse graphs, and arrays using NV-heaps, BerkeleyDB, and Stasis. Our results show that NV-heap performance scales with thread count and that data structures implemented using NV-heaps out-perform BerkeleyDB and Stasis implementations by 32x and 244x, respectively, by avoiding the operating system and minimizing other software overheads. We also quantify the cost of enforcing the safety guarantees that NV-heaps provide and measure the costs of NV-heap primitive operations.

Changing concepts of working memory.

Abstract: Working memory is widely considered to be limited in capacity, holding a fixed, small number of items, such as Miller's 'magical number' seven or Cowan's four. It has recently been proposed that working memory might better be conceptualized as a limited resource that is distributed flexibly among all items to be maintained in memory. According to this view, the quality rather than the quantity of working memory representations determines performance. Here we consider behavioral and emerging neural evidence for this proposal.

Time constraints and resource sharing in adults' working memory spans.

Abstract: This article presents a new model that accounts for working memory spans in adults, the time-based resource-sharing model. The model assumes that both components (i.e., processing and maintenance) of the main working memory tasks require attention and that memory traces decay as soon as attention is switched away. Because memory retrievals are constrained by a central bottleneck and thus totally capture attention, it was predicted that the maintenance of the items to be recalled depends on both the number of memory retrievals required by the intervening treatment and the time allowed to perform them. This number of retrievals:time ratio determines the cognitive load of the processing component. The authors show in 7 experiments that working memory spans vary as a function of this cognitive load.

The precision of visual working memory is set by allocation of a shared resource.

Abstract: The mechanisms underlying visual working memory have recently become controversial. One account proposes a small number of memory "slots," each capable of storing a single visual object with fixed precision. A contrary view holds that working memory is a shared resource, with no upper limit on the number of items stored; instead, the more items that are held in memory, the less precisely each can be recalled. Recent findings from a color report task have been taken as crucial new evidence in favor of the slot model. However, while this task has previously been thought of as a simple test of memory for color, here we show that performance also critically depends on memory for location. When errors in memory are considered for both color and location, performance on this task is in fact well explained by the resource model. These results demonstrate that visual working memory consists of a common resource distributed dynamically across the visual scene, with no need to invoke an upper limit on the number of objects represented.

Enhancing lifetime and security of PCM-based main memory with start-gap wear leveling

Abstract: Phase Change Memory (PCM) is an emerging memory technology that can increase main memory capacity in a cost-effective and power-efficient manner. However, PCM cells can endure only a maximum of 107 - 108 writes, making a PCM based system have a lifetime of only a few years under ideal conditions. Furthermore, we show that non-uniformity in writes to different cells reduces the achievable lifetime of PCM system by 20x. Writes to PCM cells can be made uniform with Wear-Leveling. Unfortunately, existing wear-leveling techniques require large storage tables and indirection, resulting in significant area and latency overheads. We propose Start-Gap, a simple, novel, and effective wear-leveling technique that uses only two registers. By combining Start-Gap with simple address-space randomization techniques we show that the achievable lifetime of the baseline 16GB PCM-based system is boosted from 5% (with no wear-leveling) to 97% of the theoretical maximum, while incurring a total storage overhead of less than 13 bytes and obviating the latency overhead of accessing large tables. We also analyze the security vulnerabilities for memory systems that have limited write endurance, showing that under adversarial settings, a PCM-based system can fail in less than one minute. We provide a simple extension to Start-Gap that makes PCM-based systems robust to such malicious attacks.