We present a novel characterization of how a program stresses cache. This characterization permits fast performance prediction in order to simulate and assist task scheduling on heterogeneous clusters. It is based on the estimation of stack distance probability distributions. The analysis requires the observation of a very small subset of memory accesses, and yields a reasonable to very accurate prediction in constant time.
For prediction, stack distances are usually recorded in a . The precision of a histogram (i.e. the range of its bins) is usually the size of a cache line. Stack distance histograms contain the number of cache misses for every cache size. Stack distance histograms are widely used for cross-platform performance prediction @cite_0 @cite_4 @cite_14 . They are lighter than application traces when the cache line size is known. However, their size is still substantial and the whole trace still needs to be collected.
@cite_0 @cite_14 @cite_4
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Attributes of a relation are not typically independent. Multidimensional histograms can be an effective tool for accurate multiattribute query selectivity estimation. In this paper, we introduce STHoles, a “workload-aware” histogram that allows bucket nesting to capture data regions with reasonably uniform tuple density. STHoles histograms are built without examining the data sets, but rather by just analyzing query results. Buckets are allocated where needed the most as indicated by the workload, which leads to accurate query selectivity estimations. Our extensive experiments demonstrate that STHoles histograms consistently produce good selectivity estimates across synthetic and real-world data sets and across query workloads, and, in many cases, outperform the best multidimensional histogram techniques that require access to and processing of the full data sets during histogram construction. In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database. > The authors propose models for both temporal and spatial locality of reference in streams of requests arriving at Web servers. They show that simple models based on document popularity alone are insufficient for capturing either temporal or spatial locality. Instead, they rely on an equivalent, but numerical, representation of a reference stream: a stack distance trace. They show that temporal locality can be characterized by the marginal distribution of the stack distance trace, and propose models for typical distributions and compare their cache performance to the traces. They also show that spatial locality in a reference stream can be characterized using the notion of self-similarity. Self-similarity describes long-range correlations in the data set, which is a property that previous researchers have found hard to incorporate into synthetic reference strings. They show that stack distance strings appear to be strongly self-similar, and provide measurements of the degree of self-similarity in the traces. Finally, they discuss methods for generating synthetic Web traces that exhibit the properties of temporal and spatial locality measured in the data. Due to the high uptake of location-based services (LBSs), large spatio-temporal datasets of moving objects' trajectories are being created every day. An important task in spatial data analytics is to service range queries by returning trajectory counts within a queried region. The question of how to keep an individual user's data private whilst enabling spatial data analytics by third parties has become an urgent research direction. Indeed, it is increasingly becoming a concern for users. To preserve privacy we discard individual trajectories and aggregate counts over a spatial and temporal partition. However the privacy gained comes at a cost to utility: trajectories passing through multiple cells and re-entering a query region, lead to inaccurate query responses. This is known as the distinct counting problem. We propose the Connection Aware Spatial Euler (CASE) histogram to address this long-standing problem. The CASE histogram maintains the connectivity of a moving object path, but does not require the ID of an object to distinguish multiple entries into an arbitrary query region. Our approach is to process trajectories offline into aggregate counts which are sent to third parties, rather than the original trajectories. We also explore modifications of our aggregate counting approach that preserve differential privacy. Theoretically and experimentally we demonstrate that our method provides a high level of accuracy compared to the best known methods for the distinct counting problem, whilst preserving privacy. We conduct our experiments on both synthetic and real datasets over two competitive Euler histogram-based methods presented in the literature. Our methods enjoy improvements to accuracy from 10 up to 70 depending on trip data and query region size, with the greatest increase seen on the Microsoft T-Drive real dataset, representing a more than tripling of accuracy.