RealTime Equalizer comes in two editions: as a standalone installation and as a Winamp plugin during deployment, you are offered the possibility to choose between the two, but bear in mind that for the latter, you need to have Winamp installed. It manages to do so by adjusting the frequency spectrum emitted during playback. Since it is the most common multidimensional data structure.RealTime Equalizer is an efficient application designed to perform modifications on audio signals in real time for a better sound experience. We put forward our tests using the R-tree Our method focuses on a leaf node retrieval and it can be simply adopted by any tree. We focus on efficiency of the disk access cost and we present an optimization of the disk access cost during range query processing. In theĬase of queries with low selectivity, the sequential scan of all tuples may be more efficient than the range query processing. Of view, the main issue of range query processing is the expensive cost of random accesses during the tree traversal. Multidimensional data is requested, the R-tree has been shown to be inefficient in many cases. The R-tree isĪ well-known structure based on the bounding of spatial near points by rectangles. Their importance lies in efficient indexing ofĭata, which have naturally multidimensional characteristics like navigation data, drawing specifications etc. Multidimensional data structures have become very popular in recent years. Although we introduce these algorithms for the R-tree, we show that these algorithms are appropriate for all multidimensional data structures with nested regions. We show optimality of these algorithms from the IO and CPU costs point of view and we compare their performance with current methods. Second, we introduce a special type of the multiple range query, the Cartesian range query. First, we show an algorithm processing a sequence of range queries. In this article, we aim our effort to processing of this type of the range query. Many real world queries can be transformed to a multiple range query: the query including more than one query rectangle. As result, these data are often stored in an array or one-dimensional index like B-tree and range queries are processed with a sequence scan. Processing range queries in a multidimensional data structure has some performance issues, especially in the case of a higher space dimension or a lower query selectivity. The range query retrieves all tuples of a multidimensional space matched by a query rectangle. These data structures support various types of queries, e.g. Multidimensional data are commonly utilized in many application areas like electronic shopping, cartography and many others. Our evaluations on real-world graphs show that ForkGraph significantly outperforms state-of-the-art graph processing systems with two orders of magnitude speedups. Besides, we theoretically prove that ForkGraph performs the same amount of work, to within a constant factor, as the fastest known sequential algorithms in FPP queries processing, which is work efficient. For inter-partition processing, we propose yielding and priority-based scheduling, to reduce redundant work in processing. For intra-partition processing, since the graph partition fits into LLC, we propose to execute each graph query with efficient sequential algorithms (in contrast with parallel algorithms in existing parallel graph processing systems) and present an atomic-free query processing by consolidating contending operations to cache-resident graph partition.
We further develop efficient intra- and inter-partition execution strategies for efficiency. To improve the cache reuse, we divide the graph into partitions each sized of LLC capacity, and the queries in an FPP are buffered and executed on the partition basis. In this paper, we propose ForkGraph, a cache-efficient FPP processing system on multi-core architectures. We find that those systems suffer from severe cache miss penalty because of the irregular and uncoordinated memory accesses in processing FPPs. We study the efficiency of state-of-the-art graph processing systems on multi-core architectures, including Ligra, Gemini, and GraphIt. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. As large graph processing emerges, we observe a costly fork-processing pattern (FPP) common in many graph algorithms.