An efficient database transitive closure algorithm

The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proceeds in two phases. In the first phase, a general graph is condensed into an acyclic one, and at the same time a special sparse matrix is formed from the acyclic graph. The second phase is the main one, in which all the page I/O operations are minimized by removing most of the redundant operations that appear in previous algorithms. Using simulation, this article also studies and examines the performance of this algorithm and compares it with the previous algorithms.     

A semantic approach to optimize linear datalog programs

In this work, we present a semantic query optimization approach to improve the efficiency of the evaluation of a subset of SQL:1999 recursive queries. Using datalog notation, we can state our main contribution as an algorithm that builds a program P′ equivalent to a given program P, when both are applied over a database d satisfying a set of functional dependencies. The input program P is a linear recursive datalog program. The new program P′ has less different variables and, sometimes, less atoms in rules, thus it is cheaper to evaluate. Using coral and ibm db2, P′ is empirically shown to be more efficient than the original program.This work is partially supported by Xunta de Galicia grant PGIDIT05SIN10502PR and Ministerio de Educación y Ciencia (PGE y FEDER) grants TIC2003-06593 and TIN2006-15071-C03-03.     

A parallel execution model of logic programs

A parallel-execution model that can concurrently exploit AND and OR parallelism in logic programs is presented. This model employs a combination of techniques in an approach to executing logic problems in parallel, making tradeoffs among number of processes, degree of parallelism, and combination bandwidth. For interpreting a nondeterministic logic program, this model (1) performs frame inheritance for newly created goals, (2) creates data-dependency graphs (DDGs) that represent relationships among the goals, and (3) constructs appropriate process structures based on the DDGs. (1) The use of frame inheritance serves to increase modularity. In contrast to most previous parallel models that have a large single process structure, frame inheritance facilitates the dynamic construction of multiple independent process structures, and thus permits further manipulation of each process structure. (2) The dynamic determination of data dependency serves to reduce computational complexity. In comparison to models that exploit brute-force parallelism and models that have fixed execution sequences, this model can reduce the number of unification and/or merging steps substantially. In comparison to models that exploit only AND parallelism, this model can selectively exploit demand-driven computation, according to the binding of the query and optional annotations. (3) The construction of appropriate process structures serves to reduce communication complexity. Unlike other methods that map DDGs directly onto process structures, this model can significantly reduce the number of data sent to a process and/or the number of communication channels connected to a process     

Optimization of parallel query execution plans in XPRS

In this paper, we describe our approach to optimization of query execution plans in XPRS, a multiuser parallel database system based on a shared memory multiprocessor and a disk array. The main difficulties in this optimization problem are the compile-time unknown parameters such as available buffer size and number of free processors, and the enormous search space of possible parallel plans. We deal with these problems with a novel two phase optimization strategy which dramatically reduces the search space and allows run time parameters without significantly compromising plan optimality. In this paper we present our two phase optimization strategy and give experimental evidence from XPRS benchmarks that indicate that it almost always produces optimal or close to optimal plans.     

Practical parallel Union-Find algorithms for transitive closure and clustering

Practical parallel algorithms, based on classical sequential Union-Find algorithms for computing transitive closures of binary relations, are described and implemented for both shared memory and distributed memory parallel computers. By practical algorithms, we mean algorithms that are efficient for parallel systems with bounded numbers of processors as opposed to algorithms where the number of processors grows with the problem size. Transitive closures are useful for decomposing many applications problems into independent subproblems. The implementations were on an ENCORE Multimax shared memory machine and an NCUBE hypercube. Our implementations indicate that transitive closure computations are intrinsically difficult for distributed memory parallel machines because of the need for global information. By contrast, our results for shared memory machines exhibited excellent speedups.Supported in part by NSF Grant DCR-8619103, ONR contract N000-86-G-0202 and DOE Grant DE-FG02-85ER25001.Supported in part by RADC contract F30602-85-C-0303.Supported in part by RADC contract F30602-85-C-0303.     

优化处理并行数据库查询的并行数据流方法

本文使用并行数据流技术优化和处理并行数据库查询的方法,提出了一整套相关算法,并给出了一个基于并行数据流方法的并行数据库查询优化处理器的完整设计.这些算法和相应的查询优化处理器已经用于作者自行设计的并行数据库管理系统原型.实践证明,并行数据流方法不仅能够快速有效地实现并行数据库管理系统,也能够有效地进行并行数据库查询的优化处理.     

Performance analysis of parallel object-oriented query processing algorithms

Advanced application domains such as computer-aided design, computer-aided software engineering, and office automation are characterized by their need to store, retrieve, and manage large quantities of data having complex structures. A number of object-oriented database management systems (OODBMS) are currently available that can effectively capture and process the complex data. The existing implementations of OODBMS outperform relational systems by maintaining and querying cross-references among related objects. However, the existing OODBMS still do not meet the efficiency requirements of advanced applications that require the execution of complex queries involving the retrieval of a large number of data objects and relationships among them. Parallel execution can significantly improve the performance of complex OO queries. In this paper, we analyze the performance of parallel OO query processing algorithms for various benchmark application domains. The application domains are characterized by specific mixes of queries of different semantic complexities. The performance of the application domains has been analyzed for various system and data parameters by running parallel programs on a 32-node transputer based parallel machine developed at the IBM Research Center at Yorktown Heights. The parallel processing algorithms, data routing techniques, and query management and control strategies have been implemented to obtain accurate estimation of controlling and processing overheads. However, generation of large complex databases for the study was impractical. Hence, the data used in the simulation have been parameterized. The parallel OO query processing algorithms analyzed in this study are based on a query graph approach rather than the traditional query tree approach. Using the query graph approach, a query is processed by simultaneously initiating the execution at several object classes, thereby, improving the parallelism. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the object references among the objects. Further, the algorithms do not generate any temporary data, thereby, reducing disk accesses. This is accomplished by marking the selected objects and by employing a two-phase query processing strategy.     

DIALOG — A dataflow model for parallel execution of logic programs

The paper presents a dataflow execution model, DIALOG, for logic programs which operates on an intermediate virtual machine. The virtual machine is granulated at clause argument level to exploit argument parallelism through unification. The model utilises a new variable binding scheme that eliminates dereference operations for accessing variables, and therefore supports OR-parallelism in the highly distributed dataflow environment. The model has been implemented in Occam. A conventional dataflow architecture in support of the model has been simulated as a testbed for the evaluation. The simulation indicates some encouraging results and suggests future improvements.     

Optimized Parallel Execution of Declarative Programs on Distributed Memory Multiprocessors

In this paper,we focus on the compiling implementation of parlalel logic language PARLOG and functional language ML on distributed memory multiprocessors.Under the graph rewriting framework, a Heterogeneous Parallel Graph Rewriting Execution Model(HPGREM)is presented firstly.Then based on HPGREM,a parallel abstact machine PAM/TGR is described.Furthermore,several optimizing compilation schemes for executing declarative programs on transputer array are proposed.The performance statistics on transputer array demonstrate the effectiveness of our model,parallel abstract machine,optimizing compilation strategies and compiler.     

On Local Stratifiability of Logic Prograns and Databases

In this paper,we deal with the problem of verifying local stratifiability of logic programs and databases presented by Przymusinski.Necessary and sufficient condition for the local stratifiability of logic programs are presented and algorithms of performing the verification are developed.Finally,we prove that a database D B containing clauses with disjunctive consequents can be easily converted into a logic program P such that D B is locally statified iff P is locally stratified.     

Using true concurrency to model execution of parallel programs

Parallel execution of a programR (intuitively regarded as a partial order) is usually modeled by sequentially executing one of the total orders (interleavings) into which it can be embedded. Our work deviates from this serialization principle by usingtrue concurrency to model parallel execution. True concurrency is represented via completions ofR tosemi total orders, called time diagrams. These orders are characterized via a set of conditions (denoted byCt), yielding orders or time diagrams which preserve some degree of the intended parallelism inR. Another way to express semi total orders is to use re-writing or derivation rules (denoted byCx) which for any programR generates a set of semi-total orders. This paper includes a classification of parallel execution into three classes according to three different types ofCt conditions. For each class a suitableCx is found and a proof of equivalence between the set of all time diagrams satisfyingCt and the set of all terminalCx derivations ofR is devised. This equivalence between time diagram conditions and derivation rules is used to define a novel notion of correctness for parallel programs. This notion is demonstrated by showing that a specific asynchronous program enforces synchronous execution, which always halts, showing that true concurrency can be useful in the context of parallel program verification.     

A wide instruction word architecture for parallel execution of logic programs coded in BSL

This paper begins by describing BSL, a new logic programming language fundamentally different from Prolog. BSL is a nondeterministic Algol-class language whose programs have a natural translation to first order logic; executing a BSL program without free variables amounts to proving the corresponding first order sentence. A new approach is proposed for parallel execution of logic programs coded in BSL, that relies on advanced compilation techniques for extracting fine grain parallelism from sequential code. We describe a new “Very Long Instruction Word” (VLIW) architecture for parallel execution of BSL programs. The architecture, now being designed at the IBM Thomas J. Watson Research Center, avoids the synchronization and communication delays (normally associated with parallel execution of logic programs on multiprocessors), by determining data dependences between operations at compile time, and by coupling the processing elements very tightly, via a single central shared register file. A simulator for the architecture has been implemented and some simulation results are reported in the paper, which are encouraging.     

Continuation-based control in the implementation of parallel logic programs

Continuations are used to define the flow of messages between low level tasks in a parallel logic programming language. A combination of compiler and runtime operations reduces message traffic by up to 50% when success continuations are passed as parameters in messages that start new processes. Continuations are also the key to fast task switching, a critical operation in this fine grain parallel system. Data from sample programs shows the effectiveness of continuations in reducing message traffic and the speed with which task switches are performed on a typical host architecture.Supported by NSF Grant CCR-8707177 and grants from Motorola, Inc, and Hewlett-Packard Corp.     

Considering data skew factor in multi-way join query optimization for parallel execution

A consensus on parallel architecture for very large database management has emerged. This architecture is based on a shared-nothing hardware organization. The computation model is very sensitive to skew in tuple distribution, however. Recently, several parallel join algorithms with dynamic load balancing capabilities have been proposed to address this issue, but none of them consider multi-way join problems. In this article we propose a dynamic load balancing technique for multi-way joins, and investigate the effect of load balancing on query optimization. In particular, we present a join-ordering strategy that takes load-balancing issues into consideration. Our performance study indicates that the proposed query optimization technique can provide very impressive performance improvement over conventional approaches.An earlier version of this article was presented at the 1993 International Conference on Parallel and Distributed Information Systems in San Diego, California, U.S.A.     

Implementation and evaluation of parallel query processing algorithms and data partitioning heuristics in object-oriented databases

Object-oriented database management systems (OODBMSs) provide rich facilities for the modeling and processing of structural as well as behavioral properties of complex application objects. However, due to their inherent generality and continuously evolving functionalities, efficient implementations are important for these OODBMSs to support the present and future applications, particularly when the databases are very large. In this paper, we present several parallel, multi-wavefront algorithms based on two processing approaches, i.e., identification and elimination approaches, to verify association patterns specified in queries. Both approaches allow more processors to operate concurrently on a query than the traditional tree-structured query processing approach, thus introducing a higher degree of parallelism in query processing. A heuristic method is presented for partitioning an object-oriented database (OODB). The main consideration for partitioning the database is load balancing. This method also tries to reduce the communication time by reducing the length of the path that wavefronts need to be propagated. Multiple wavefront algorithms based on the two approaches for tree-structured queries have been implemented on an nCUBE 2 parallel computer. The implementation of the query processor allows multiple queries to be executed simultaneously. This implementation provides an environment for evaluating the algorithms and the heuristic method for partitioning the database. The evaluation results are presented in this paper.Recommended by: Patrick Valduriez     

A backtracking algorithm for the stream AND-parallel execution of logic programs

We present the first backtracking algorithm for stream AND-parallel logic programs. It relies on compile-time knowledge of the dataflow graph of each clause to let it figure out efficiently which goals to kill or restart when a goal fails. This crucial information, which we derive from mode declarations, was not available at compile-time in any previous stream AND-parallel system. We show that modes can increase the precision of the backtracking algorithm, though our algorithm allows this precision to be traded off against overhead on a procedure-by-procedure and call-by-call basis. The modes also allow our algorithm to handle efficiently programs that manipulate partially instantiated data structures and an important class of programs with circular dependency graphs. On code that does not need backtracking, the efficiency of our algorithm approaches that of the committed-choice languages; on code that does need backtracking its overhead is comparable to that of the independent AND-parallel backtracking algorithms.     

Adaptive execution techniques of parallel programs for multiprocessors

In simultaneous multithreading (SMT) multiprocessors, using all the available threads (logical processors) to run a parallel loop is not always beneficial due to the interference between threads and parallel execution overhead. To maximize the performance of a parallel loop on an SMT multiprocessor, it is important to find an appropriate number of threads for executing the parallel loop. This article presents adaptive execution techniques that find a proper execution mode for each parallel loop in a conventional loop-level parallel program on SMT multiprocessors. A compiler preprocessor generates code that, based on dynamic feedbacks, automatically determines at run time the optimal number of threads for each parallel loop in the parallel application. We evaluate our technique using a set of standard numerical applications and running them on a real SMT multiprocessor machine with 8 hardware contexts. Our approach is general enough to work well with other SMT multiprocessor or multicore systems.     

ConceptBase — A deductive object base for meta data management

Deductive object bases attempt to combine the advantages of deductive relational databases with those of object-oriented databases. We review modeling and implementation issues encountered during the development of ConceptBase, a prototype deductive object manager supporting the Telos object model. Significant features include: 1) The symmetric treatment of object-oriented, logic-oriented and graph-oriented perspectives, 2) an infinite metaclass hierarchy as a prerequisite for extensibility and schema evolution, 3) a simple yet powerful formal semantics used as the basis for implementation, 4) a client-server architecture supporting collaborative work in a wide-area setting. Several application experiences demonstrate the value of the approach especially in the field of meta data management.