2,820 research outputs found
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
Cache Hierarchy Inspired Compression: a Novel Architecture for Data Streams
We present an architecture for data streams based on structures typically found in web cache hierarchies. The main idea is to build a meta level analyser from a number of levels constructed over time from a data stream. We present the general architecture for such a system and an application to classification. This architecture is an instance of the general wrapper idea allowing us to reuse standard batch learning algorithms in an inherently incremental learning environment. By artificially generating data sources we demonstrate that a hierarchy containing a mixture of models is able to adapt over time to the source of the data. In these experiments the hierarchies use an elementary performance based replacement policy and unweighted voting for making classification decisions
Near-Memory Address Translation
Memory and logic integration on the same chip is becoming increasingly cost
effective, creating the opportunity to offload data-intensive functionality to
processing units placed inside memory chips. The introduction of memory-side
processing units (MPUs) into conventional systems faces virtual memory as the
first big showstopper: without efficient hardware support for address
translation MPUs have highly limited applicability. Unfortunately, conventional
translation mechanisms fall short of providing fast translations as
contemporary memories exceed the reach of TLBs, making expensive page walks
common.
In this paper, we are the first to show that the historically important
flexibility to map any virtual page to any page frame is unnecessary in today's
servers. We find that while limiting the associativity of the
virtual-to-physical mapping incurs no penalty, it can break the
translate-then-fetch serialization if combined with careful data placement in
the MPU's memory, allowing for translation and data fetch to proceed
independently and in parallel. We propose the Distributed Inverted Page Table
(DIPTA), a near-memory structure in which the smallest memory partition keeps
the translation information for its data share, ensuring that the translation
completes together with the data fetch. DIPTA completely eliminates the
performance overhead of translation, achieving speedups of up to 3.81x and
2.13x over conventional translation using 4KB and 1GB pages respectively.Comment: 15 pages, 9 figure
Design Guidelines for High-Performance SCM Hierarchies
With emerging storage-class memory (SCM) nearing commercialization, there is
evidence that it will deliver the much-anticipated high density and access
latencies within only a few factors of DRAM. Nevertheless, the
latency-sensitive nature of memory-resident services makes seamless integration
of SCM in servers questionable. In this paper, we ask the question of how best
to introduce SCM for such servers to improve overall performance/cost over
existing DRAM-only architectures. We first show that even with the most
optimistic latency projections for SCM, the higher memory access latency
results in prohibitive performance degradation. However, we find that
deployment of a modestly sized high-bandwidth 3D stacked DRAM cache makes the
performance of an SCM-mostly memory system competitive. The high degree of
spatial locality that memory-resident services exhibit not only simplifies the
DRAM cache's design as page-based, but also enables the amortization of
increased SCM access latencies and the mitigation of SCM's read/write latency
disparity.
We identify the set of memory hierarchy design parameters that plays a key
role in the performance and cost of a memory system combining an SCM technology
and a 3D stacked DRAM cache. We then introduce a methodology to drive
provisioning for each of these design parameters under a target
performance/cost goal. Finally, we use our methodology to derive concrete
results for specific SCM technologies. With PCM as a case study, we show that a
two bits/cell technology hits the performance/cost sweet spot, reducing the
memory subsystem cost by 40% while keeping performance within 3% of the best
performing DRAM-only system, whereas single-level and triple-level cell
organizations are impractical for use as memory replacements.Comment: Published at MEMSYS'1
Adaptive TTL-Based Caching for Content Delivery
Content Delivery Networks (CDNs) deliver a majority of the user-requested
content on the Internet, including web pages, videos, and software downloads. A
CDN server caches and serves the content requested by users. Designing caching
algorithms that automatically adapt to the heterogeneity, burstiness, and
non-stationary nature of real-world content requests is a major challenge and
is the focus of our work. While there is much work on caching algorithms for
stationary request traffic, the work on non-stationary request traffic is very
limited. Consequently, most prior models are inaccurate for production CDN
traffic that is non-stationary.
We propose two TTL-based caching algorithms and provide provable guarantees
for content request traffic that is bursty and non-stationary. The first
algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic
approximation approach. Given a feasible target hit rate, we show that the hit
rate of d-TTL converges to its target value for a general class of bursty
traffic that allows Markov dependence over time and non-stationary arrivals.
The second algorithm called f-TTL uses two caches, each with its own TTL. The
first-level cache adaptively filters out non-stationary traffic, while the
second-level cache stores frequently-accessed stationary traffic. Given
feasible targets for both the hit rate and the expected cache size, f-TTL
asymptotically achieves both targets. We implement d-TTL and f-TTL and evaluate
both algorithms using an extensive nine-day trace consisting of 500 million
requests from a production CDN server. We show that both d-TTL and f-TTL
converge to their hit rate targets with an error of about 1.3%. But, f-TTL
requires a significantly smaller cache size than d-TTL to achieve the same hit
rate, since it effectively filters out the non-stationary traffic for
rarely-accessed objects
Supercomputing visualization made simple
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. [39]-[40]).In this thesis, we propose a solution for remote visualization for supercomputers. Our solution consists of two tools that help users visualize data from high performance computers. The first one takes advantage of the Web and AJAX technology [25], is simple, light weight and does not require any pre-installation which can be a perfect tool for demonstration supercomputing data. The second tool, a 3D Viewer on MATLAB Star-P [8], is to utilize more resources in the user's workstation to achieve better quality visualization and more flexibility in data navigation and analysis. Both solutions strive to create a simple and user-friendly framework that supports researchers' goals to create, analyze, test and debug numerical algorithms in supercomputing world.by Huy Nguyen.S.M
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