A Survey of Techniques for Architecting TLBs

“Translation lookaside buffer” (TLB) caches virtual to physical address translation information and is used in systems ranging from embedded devices to high-end servers. Since TLB is accessed very frequently and a TLB miss is extremely costly, prudent management of TLB is important for improving performance and energy efficiency of processors. In this paper, we present a survey of techniques for architecting and managing TLBs. We characterize the techniques across several dimensions to highlight their similarities and distinctions. We believe that this paper will be useful for chip designers, computer architects and system engineers

Sandbox prefetching: safe run-time evaluation of aggressive prefetchers

pre-printMemory latency is a major factor in limiting CPU per- formance, and prefetching is a well-known method for hiding memory latency. Overly aggressive prefetching can waste scarce resources such as memory bandwidth and cache capacity, limiting or even hurting performance. It is therefore important to employ prefetching mechanisms that use these resources prudently, while still prefetching required data in a timely manner. In this work, we propose a new mechanism to deter-mine at run-time the appropriate prefetching mechanism for the currently executing program, called Sandbox Prefetching. Sandbox Prefetching evaluates simple, aggressive offset prefetchers at run-time by adding the prefetch address to a Bloom filter, rather than actually fetching the data into the cache. Subsequent cache accesses are tested against the contents of the Bloom filter to see if the aggressive prefetcher under evaluation could have accurately prefetched the data, while simultaneously testing for the existence of prefetchable streams. Real prefetches are performed when the accuracy of evaluated prefetchers exceeds a threshold. This method combines the ideas of global pattern confirmation and immediate prefetching action to achieve high performance. Sandbox Prefetching improves performance across the tested workloads by 47.6% compared to not using any prefetching, and by 18.7% compared to the Feedback Directed Prefetching technique. Performance is also improved by 1.4% compared to the Access Map Pattern Matching Prefetcher, while incurring consid- erably less logic and storage overheads

Dynamic Memory Optimization using Pool Allocation and Prefetching

Heap memory allocation plays an important role in modern applications. Conventional heap allocators, however, generally ignore the underlying memory hierarchy of the system, favoring instead a low runtime overhead and fast response times. Unfortunately, with little concern for the memory hierarchy, the data layout may exhibit poor spatial locality, and degrade cache performance. In this paper, we describe a dynamic heap allocation scheme called pool allocation. The strategy aims to improve cache performance by inspecting memory allocation requests, and allocating memory from appropriate heap pools as dictated by the requesting context. The advantages are two fold. First, by pooling together data with a common context, we expect to improve spatial locality, as data fetched to the caches will contain fewer items from different contexts. If the allocation patterns are closely matched to the traversal patterns, the end result is faster memory performance. Second, by pooling heap objects, we expect access patterns to exhibit more regularity, thus creating more opportunities for data prefetching. Our dynamic memory optimizer exploits the increased regularity to insert prefetch instructions at runtime. The optimizations are implemented in DynamoRIO, a dynamic optimization framework. We evaluate the work using various benchmarks, and measure a 17% speedup over gcc -O3 on an Athlon MP, and a 13% speedup on a Pentium 4.Singapore-MIT Alliance (SMA

Best-Offset Hardware Prefetching

International audienceHardware prefetching is an important feature of modern high-performance processors. When the application working set is too large to fit in on-chip caches, disabling hardware prefetchers may result in severe performance reduction. A new prefetcher was recently introduced, the Sandbox prefetcher, that tries to find dynamically the best prefetch offset using the sandbox method. The Sandbox prefetcher uses simple hardware and was shown to be quite effective. However, the sandbox method does not take into account prefetch timeliness. We propose an offset prefetcher with a new method for selecting the prefetch offset that takes into account prefetch timeliness. We show that our Best-Offset prefetcher outperforms the Sandbox prefetcher on the SPEC CPU2006 benchmarks , with equally simple hardware

ISIM: The simulator for the impulse adaptable memory system

technical reportThis document describes ISIM, the simulator for the Impulse Adaptable Memory System. Impulse adds two new features to a conventional memory system. First, it supports a configurable, extra level of address remapping at the memory controller. Second, it supports prefetching at the memory controller. consequently, two new units, a remapping controller and a memory controller cache, are added to a traditional memory system to support the new Impulse features. ISIM is based on Paint, a PA-RISC instruction set interpreter. ISIM extends Paint with a detailed Impulse memory system model which includes a primary data cache, a secondary data cache, a system bus, an Impulse memory controller, and a renovated DRAM backend. Note that this document focuses on the Impulse extensions only. The reader should consult the Paint technical report [2] for an overview of the Paint simulation environment and terminology

Graph prefetching using data structure knowledge

Searches on large graphs are heavily memory latency bound, as a result of many high latency DRAM accesses. Due to the highly irregular nature of the access patterns involved, caches and prefetchers, both hardware and software, perform poorly on graph workloads. This leads to CPU stalling for the majority of the time. However, in many cases the data access pattern is well defined and predictable in advance, many falling into a small set of simple patterns. Although existing implicit prefetchers cannot bring significant benefit, a prefetcher armed with knowledge of the data structures and access patterns could accurately anticipate applications' traversals to bring in the appropriate data. This paper presents a design of an explicitly configured prefetcher to improve performance for breadth-first searches and sequential iteration on the efficient and commonly-used compressed sparse row graph format. By snooping L1 cache accesses from the core and reacting to data returned from its own prefetches, the prefetcher can schedule timely loads of data in advance of the application needing it. For a range of applications and graph sizes, our prefetcher achieves average speedups of 2.3x, and up to 3.3x, with little impact on memory bandwidth requirements.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC), through grant references EP/K026399/1 and EP/M506485/1, and ARM Ltd.This is the author accepted manuscript. The final version is available from ACM at http://dx.doi.org/10.1145/2925426.2926254