Macro Data Flow Graphs Research Articles

Parallel extremal optimization in processor load balancing for distributed applications

The paper concerns parallel methods for extremal optimization (EO) applied in processor load balancing in execution of distributed programs. In these methods EO algorithms detect an optimized strategy of tasks migration leading to reduction of program execution time. We use an improved EO algorithm with guided state changes (EO-GS) that provides parallel search for next solution state during solution improvement based on some knowledge of the problem. The search is based on two-step stochastic selection using two fitness functions which account for computation and communication assessment of migration targets. Based on the improved EO-GS approach we propose and evaluate several versions of the parallelization methods of EO algorithms in the context of processor load balancing. Some of them use the crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluated by experiments with simulated load balancing in execution of distributed programs represented as macro data flow graphs. Load balancing based on so parallelized improved EO provides better convergence of the algorithm, smaller number of task migrations to be done and reduced execution time of applications.

The optimization method of a macro dataflow graph for reconfigurable computing systems

The optimization method of a macro dataflow graph for reconfigurable computing systems

Byte-code scheduling of Java programs with branches for desktop grid

A method for an introductory optimization of multithreaded Java programs for execution on clusters of Java Virtual Machines (JVMs) inside desktop grids is presented. It is composed of two stages. In the first stage, a clustering algorithm is applied to extended macro data flow graphs generated on the basis of the byte-code compiled for multithreaded Java programs. These graphs account for data and control dependencies in programs including conditional branch instructions annotated by branch statistics driven from execution traces for representative sets of data. In the second stage, a list scheduling is performed based on the Earliest Task First (ETF) heuristics in which node mapping on JVMs accounts for mutually exclusive paths outgoing from conditional branch nodes. The presented object placement optimization algorithm is a part of the DG-ADAJ environment.

Compilation of parallel multimedia computations—extending retiming theory and Amdahl's law

Multimedia applications (also called multimedia systems) operate on datastreams, which are periodic sequences of data elements, called datasets . A large class of multimedia applications is described by the macro-dataflow graph model, with nodes representing parallelizable tasks, and arcs representing communication. This paper examines how such multimedia applications can be compiled to run efficiently on parallel machines, by optimizing both throughput ( T ) and latency ( L ), using two techniques, based on task speedup functions. The first step chooses an appropriate pipeline structure for the system (task clustering) . The second step exploits the dataset parallelism intrinsic in the periodic datastream, and runs multiple datasets in parallel (task/cluster multiplicity) for each clustering. The key find-of this research areA The best task clustering depends on system throughput. In general skewed parallelism profiles are desirable i.e. tasks with good speedup and tasks with poor speedup are in separate clusters. Indeed the maximal throughput and minimal latency can be simultaneously attained in the limiting case of a maximally skewed distribution. This result can be viewed as a generalization of Amdahl's law for real-time applications.B Optimal dataset multiplicity for a specific clustering can be determined by extending retiming theory [1] to include parallel resource allocation. In this process, counter-intuitive relaxation regions often appear, wherein by increasing dataset multiplicity, throughput is increased and latency simultaneously reduced (a free lunch).The techniques have been used for compiling real-time image-processing problems on an NCUBE-2 multiprocessor, and show substantial performance gains.

On parallelization of static scheduling algorithms

Most static algorithms that schedule parallel programs represented by macro dataflow graphs are sequential. This paper discusses the essential issues pertaining to parallelization of static scheduling and presents two efficient parallel scheduling algorithms. The proposed algorithms have been implemented on an Intel Paragon machine and their performances have been evaluated. These algorithms produce high-quality scheduling and are much faster than existing sequential and parallel algorithms.

SIMULTANEOUS ALLOCATION AND SCHEDULING USING CONVEX PROGRAMMING TECHNIQUES

Simultaneous exploitation of task and data parallelism provides significant benefits for many applications. The basic approach for exploiting task and data parallelism is to use a task graph representation (Macro Dataflow Graph) for programs to decide on the degree of data parallelism to be used for each task (allocation) and an execution order for the tasks (scheduling). Previously, we presented a two step approach for allocation and scheduling by considering the two steps to be independent of each other. In this paper, we present a new simultaneous approach which uses constraints to model the scheduler during allocation. The new simultaneous approach provides significant benefits over our earlier approach for the benchmark task graphs that we have considered.

Hierarchical compilation of macro dataflow graphs for multiprocessors with local memory

This paper presents a hierarchical approach for compiling macro dataflow graphs for multiprocessors with local memory. Macro dataflow graphs comprise several nodes (or macro operations) that must be executed subject to prespecified precedence constraints. Programs consisting of multiple nested loops, where the precedence constraints between the loops are known, can be viewed as macro dataflow graphs. The hierarchical compilation approach comprises a processor allocation phase followed by a partitioning phase. In the processor allocation phase, using estimated speedup functions for the macro nodes, computationally efficient techniques establish the sequencing and parallelism of macro operations for close-to-optimal run-times. The second phase partitions the computations in each macro node to maximize communication locality for the level of parallelism determined by the processor allocation phase. The same approach can also be used for programs consisting of multiple loop nests, when each of the nested loops can be characterized by a speedup function. These ideas have been implemented in a prototype structure-driven compiler, SDC, for expressions of matrix operations. The paper presents the performance of the compiler for several matrix expressions on a simulator of the Alewife multiprocessor.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A programming aid for hypercube architectures

A program development tool that automatically performs scheduling and synchronization insertion for hypercube systems is presented in this paper. We use a programming methodology in which a program is written as a set of procedures called from the main program. This program is converted into a macro dataflow graph which is used by the tool to generate executable code for hypercube machines. The program restructured by our tool outperformed the manually developed code, while increasing the program development productivity at the same time.