Hardware trends have produced an increasing disparity between processor speeds and memory access times. While a variety of techniques for tolerating or reducing memory latency have been proposed, these are rarely successful for pointer-manipulating programs.This paper explores a complementary approach that attacks the source (poor reference locality) of the problem rather than its manifestation (memory latency). It demonstrates that careful data organization and layout provides an essential mechanism to improve the cache locality of pointer-manipulating programs and consequently, their performance. It explores two placement techniques---clustering and coloring---that improve cache performance by increasing a pointer structure's spatial and temporal locality, and by reducing cache-conflicts.To reduce the cost of applying these techniques, this paper discusses two strategies---cache-conscious reorganization and cache-conscious allocation---and describes two semi-automatic tools---ccmorph and ccmalloc---that use these strategies to produce cache-conscious pointer structure layouts. ccmorph is a transparent tree reorganizer that utilizes topology information to cluster and color the structure. ccmalloc is a cache-conscious heap allocator that attempts to co-locate contemporaneously accessed data elements in the same physical cache block. Our evaluations, with microbenchmarks, several small benchmarks, and a couple of large real-world applications, demonstrate that the cache-conscious structure layouts produced by ccmorph and ccmalloc offer large performance benefits---in most cases, significantly outperforming state-of-the-art prefetching.

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Cache-conscious structure layout

In this paper, we have developed a HiTi (Hierarchical MulTi) graph model for structuring large topographical road maps to speed up the minimum cost route computation. The HiTi graph model provides a novel approach to abstracting and structuring a topographical road map in a hierarchical fashion. We propose a new shortest path algorithm named SPAH, which utilizes HiTi graph model of a topographical road map for its computation. We give the proof for the optimality of SPAH. Our performance analysis of SPAH on grid graphs showed that it significantly reduces the search space over existing methods. We also present an in-depth experimental analysis of HiTi graph method by comparing it with other similar works on grid graphs. Within the HiTi graph framework, we also propose a parallel shortest path algorithm named ISPAH. Experimental results show that inter query shortest path problem provides more opportunity for scalable parallelism than the intra query shortest path problem.

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An efficient path computation model for hierarchically structured topographical road maps

An important feature of database support for expert systems is the ability of the database to answer queries regarding the existence of a path from one node to another in the directed graph underlying some database relation. Given just the database relation, answering such a query is time-consuming, but given the transitive closure of the database relation a table look-up suffices. We present an indexing scheme that permits the storage of the pre-computed transitive closure of a database relation in a compressed form. The existence of a specified tuple in the closure can be determined from this compressed store by a single look-up followed by an index comparision. We show how to add nodes and arcs to the compressed closure incrementally. We also suggest how this compression technique can be used to reduce the effort required to compute the transitive closure.

A compression technique to materialize transitive closure

Current spatial database management systems (SDBMS) provide efficient access methods and operators for point and range queries over collections of spatial points, line segments, and polygons. However, it is not clear if existing spatial access methods can efficiently support network computations which traverse line segments in a spatial network based on connectivity rather than geographic proximity. The expected I/O cost for many network operations can be reduced by maximizing the Weighted Connectivity Residue Ratio (WCRR), i.e., the chance that a pair of connected nodes that are more likely to be accessed together are allocated to a common page of the file. CCAM is an access method for general networks that uses connectivity clustering. CCAM supports the operations of insert, delete, create, and find as well as the new operations, get-A-successor and get-successors, which retrieve one or all successors of a node to facilitate aggregate computations on networks. The nodes of the network are assigned to disk pages via a graph partitioning approach to maximize the WCRR. CCAM includes methods for static clustering, as well as dynamic incremental reclustering, to maintain high WCRR in the face of updates, without incurring high overheads. We also describe possible modifications to improve the WCRR that can be achieved by existing spatial access methods. Experiments with network computations on the Minneapolis road map show that CCAM outperforms existing access methods, even though the proposed modifications also substantially improve the performance of existing spatial access methods.

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CCAM: a connectivity-clustered access method for networks and network computations

The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. To improve the CPU cache performance, there are studies to support searching including cache-oblivious, and cache-conscious trees. In this paper, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. The approaches dealing with trees are not applicable to graphs which are complex in nature. In this paper, we explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other 9 possible graph orderings (such as the one obtained by METIS) using 8 large real graphs and 9 representative graph algorithms. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.

Speedup Graph Processing by Graph Ordering

A DAG (direct acyclic graph) is an important data structure which requires efficient support in CAD (computer-aided design) databases. It typically arise from the design hierarchy, which describes complex designs in terms of subdesigns. A study is made of the properties of the three types of clustered sequences of nodes for hierarchies and DAGs, and algorithms are developed for generating the clustered sequences, retrieving the descendants of a given node, and inserting new nodes into existing clustered sequences of nodes which preserve their clustering properties. The performance of the clustering sequences is compared. >

J. Banerjee

Papers

Clustering a DAG for CAD databases