This paper describes the design criteria and implementation details of a dynamic storage allocator for real-time systems. The main requirements that have to be considered when designing a new allocator are concerned with temporal and spatial constraints. The proposed algorithm, called TLSF (two-level segregated fit), has an asymptotic constant cost, O(1), maintaining a fast response time (less than 200 processor instructions on a x86 processor) and a low level of memory usage (low fragmentation). TLSF uses two levels of segregated lists to arrange free memory blocks and an incomplete search policy. This policy is implemented with word-size bitmaps and logical processor instructions. Therefore, TLSF can be categorized as a good-fit allocator. The incomplete search policy is shown also to be a good policy in terms of fragmentation. The fragmentation caused by TLSF is slightly smaller (better) than that caused by best fit (which is one of the best allocators regarding memory fragmentation). In order to evaluate the proposed allocator, three analyses are presented in this paper. The first one is based on worst-case scenarios. The second one provides a detailed consideration of the execution cost of the internal operations of the allocator and its fragmentation. The third analysis is a comparison with other well-known allocators from the temporal (number of cycles and processor instructions) and spatial (fragmentation) points of view. In order to compare them, a task model has been presented. Copyright © 2007 John Wiley & Sons, Ltd.

/pdf/implementation-of-a-constant-time-dynamic-storage-allocator-1mbwo1hcax.pdf

Implementation of a constant-time dynamic storage allocator

Uncertainty analysis is critical for conducting reservoir performance prediction. However, it is challenging because it relies on (1) massive modeling-related, geographically distributed, terabyte, or even petabyte scale data sets (geoscience and engineering data), (2) needs to rapidly perform hundreds or thousands of flow simulations, being identical runs with different models calculating the impacts of various uncertainty factors, (3) an integrated, secure, and easy-to-use problem-solving toolkit to assist uncertainty analysis. We leverage Grid computing technologies to address these challenges. We design and implement an integrated problem-solving environment ResGrid to effectively improve reservoir uncertainty analysis. The ResGrid consists of data management, execution management, and a Grid portal. Data Grid tools, such as metadata, replica, and transfer services, are used to meet massive size and geographically distributed characteristics of data sets. Workflow, task farming, and resource allocation are used to support large-scale computation. A Grid portal integrates the data management and the computation solution into a unified easy-to-use interface, enabling reservoir engineers to specify uncertainty factors of interest and perform large-scale reservoir studies through a web browser. The ResGrid has been used in petroleum engineering. Copyright © 2008 John Wiley & Sons, Ltd.

A Grid-enabled problem-solving environment for advanced reservoir uncertainty analysis

We explore advances in Java Virtual Machine (JVM) technology along with new high performance I/O libraries in Java 1.4, and find that Java is increasingly an attractive platform for scientific cluster-based message passing codes.

MPJava: High-Performance Message Passing in Java Using Java.nio

The use of Java for parallel programming on clusters according to the message-passing paradigm is an attractive choice. In this case, the overall application performance will largely depend on the performance of the underlying Java message-passing library. This paper evaluates, models and compares the performance of MPI-like point-to-point and collective communication primitives from selected Java message-passing implementations on clusters with different interconnection networks. We have developed our own micro-benchmark suite to characterize the message-passing communication overhead and thus derive analytical latency models.

/pdf/performance-analysis-of-java-message-passing-libraries-on-3bh4v9bail.pdf

Performance analysis of Java message-passing libraries on fast Ethernet, Myrinet and SCI clusters

This paper presents CRAC, an environment dedicated to design efficient asynchronous iterative algorithms for a grid architecture. Those algorithms are particularly suited for grid architecture since they naturally allow to overlap communications by computations. Each processor computes its iterations freely without any synchronization with its neighbors. All the characteristics of CRAC are described. A real application using four distant clusters, with a total of 120 processors, shows the interest of this environment and of asynchronous algorithms.

/pdf/crac-a-grid-environment-to-solve-scientific-applications-3sedpiipoh.pdf

CRAC: a Grid Environment to Solve Scientific Applications with Asynchronous Iterative Algorithms

The Message Passing Interface (MPI) standard provides a uniform Application Programmers Interface (API) that abstracts the underlying hardware from the parallel applications. Recent research efforts have extended the MPI standard to Java either through wrapper implementations or as subsets of larger parallel infrastructures. In this paper, we describe JMPI, a reference implementation of MPI developed at the Architecture and Real-Time Laboratory at the University of Massachusetts Amherst. In this implementation, we explore using Java's Remote Method Invocation (RMI), Object Serialization and Introspection technologies for message passing. We discuss the architecture of our implementation, adherence to emerging standards for MPI bindings for Java, our performance results, and future research direction.

JMPI: implementing the message passing standard in Java

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JMPI: implementing the message passing standard in Java