Ceriel J. H. Jacobs

Ibis: a Flexible and Efficient Java based Grid Programming Environment

In computational Grids, performance‐hungry applications need to simultaneously tap the computational power of multiple, dynamically available sites. The crux of designing Grid programming environments stems exactly from the dynamic availability of compute cycles: Grid programming environments (a) need to be portable to run on as many sites as possible, (b) they need to be flexible to cope with different network protocols and dynamically changing groups of compute nodes, while (c) they need to provide efficient (local) communication that enables high‐performance computing in the first place. Existing programming environments are either portable (Java), or flexible (Jini, Java Remote Method Invocation or (RMI)), or they are highly efficient (Message Passing Interface). No system combines all three properties that are necessary for Grid computing. In this paper, we present Ibis, a new programming environment that combines Javas ‘run everywhere’ portability both with flexible treatment of dynamically available networks and processor pools, and with highly efficient, object‐based communication. Ibis can transfer Java objects very efficiently by combining streaming object serialization with a zero‐copy protocol. Using RMI as a simple test case, we show that Ibis outperforms existing RMI implementations, achieving up to nine times higher throughputs with trees of objects. Copyright

Performance evaluation of the Orca shared-object system

Orca is a portable, object-based distributed shared memory (DSM) system. This article studies and evaluates the design choices made in the Orca system and compares Orca with other DSMs. The article gives a quantitative analysis of Orcas coherence protocol (based on write-updates with function shipping), the totally ordered group communication protocol, the strategy for object placement, and the all-software, user-space architecture. Performance measurements for 10 parallel applications illustrate the trade-offs made in the design of Orca and show that essentially the right design decisions have been made. A write-update protocol with function shipping is effective for Orca, especially since it is used in combination with techniques that avoid replicating objects that have a low read/write ratio. The overhead of totally ordered group communication on application performance is low. The Orca system is able to make near-optimal decisions for object placement and replication. In addition, the article compares the performance of Orca with that of a page-based DSM (TreadMarks) and another object-based DSM (CRL). It also analyzes the communication overhead of the DSMs for several applications. All performance measurements are done on a 32-node Pentium Pro cluster with Myrinet and Fast Ethernet networks. The results show that Orca programs send fewer messages and less data than the TreadMarks and CRL programs and obtain better speedups.

The distributed ASCI Supercomputer project

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project.

Efficient Java RMI for Parallel Programming

Java offers interesting opportunities for parallel computing. In particular, Java Remote Method Invocation (RMI) provides a flexible kind of remote procedure call (RPC) that supports polymorphism. Suns RMI implementation achieves this kind of flexibility at the cost of a major runtime overhead. The goal of this article is to show that RMI can be implemented efficiently, while still supporting polymorphism and allowing interoperability with Java Virtual Machines (JVMs). We study a new approach for implementing RMI, using a compiler-based Java system called Manta. Manta uses a native (static) compiler instead of a just-in-time compiler. To implement RMI efficiently, Manta exploits compile-time type information for generating specialized serializers. Also, it uses an efficient RMI protocol and fast low-level communication protocols.A difficult problem with this approach is how to support polymorphism and interoperability. One of the consequences of polymorphism is that an RMI implementation must be able to download remote classes into an application during runtime. Manta solves this problem by using a dynamic bytecode compiler, which is capable of compiling and linking bytecode into a running application. To allow interoperability with JVMs, Manta also implements the Sun RMI protocol (i.e., the standard RMI protocol), in addition to its own protocol.We evaluate the performance of Manta using benchmarks and applications that run on a 32-node Myrinet cluster. The time for a null-RMI (without parameters or a return value) of Manta is 35 times lower than for the Sun JDK 1.2, and only slightly higher than for a C-based RPC protocol. This high performance is accomplished by pushing almost all of the runtime overhead of RMI to compile time. We study the performance differences between the Manta and the Sun RMI protocols in detail. The poor performance of the Sun RMI protocol is in part due to an inefficient implementation of the protocol. To allow a fair comparison, we compiled the applications and the Sun RMI protocol with the native Manta compiler. The results show that Mantas null-RMI latency is still eight times lower than for the compiled Sun RMI protocol and that Mantas efficient RMI protocol results in 1.8 to 3.4 times higher speedups for four out of six applications.

Satin: A high-level and efficient grid programming model

Computational grids have an enormous potential to provide compute power. However, this power remains largely unexploited today for most applications, except trivially parallel programs. Developing parallel grid applications simply is too difficult. Grids introduce several problems not encountered before, mainly due to the highly heterogeneous and dynamic computing and networking environment. Furthermore, failures occur frequently, and resources may be claimed by higher-priority jobs at any time. In this article, we solve these problems for an important class of applications: divide-and-conquer. We introduce a system called Satin that simplifies the development of parallel grid applications by providing a rich high-level programming model that completely hides communication. All grid issues are transparently handled in the runtime system, not by the programmer. Satins programming model is based on Java, features spawn-sync primitives and shared objects, and uses asynchronous exceptions and an abort mechanism to support speculative parallelism. To allow an efficient implementation, Satin consistently exploits the idea that grids are hierarchically structured. Dynamic load-balancing is done with a novel cluster-aware scheduling algorithm that hides the long wide-area latencies by overlapping them with useful local work. Satins shared object model lets the application define the consistency model it needs. If an application needs only loose consistency, it does not have to pay high performance penalties for wide-area communication and synchronization. We demonstrate how grid problems such as resource changes and failures can be handled transparently and efficiently. Finally, we show that adaptivity is important in grids. Satin can increase performance considerably by adding and removing compute resources automatically, based on the applications requirements and the utilization of the machines and networks in the grid. Using an extensive evaluation on real grids with up to 960 cores, we demonstrate that it is possible to provide a simple high-level programming model for divide-and-conquer applications, while achieving excellent performance on grids. At the same time, we show that the divide-and-conquer model scales better on large systems than the master-worker approach, since it has no single central bottleneck.

DynamiTE: Parallel Materialization of Dynamic RDF Data

One of the main advantages of using semantically annotated data is that machines can reason on it, deriving implicit knowledge from explicit information. In this context, materializing every possible implicit derivation from a given input can be computationally expensive, especially when considering large data volumes. Most of the solutions that address this problem rely on the assumption that the information is static, i.e., that it does not change, or changes very infrequently. However, the Web is extremely dynamic: online newspapers, blogs, social networks, etc., are frequently changed so that outdated information is removed and replaced with fresh data. This demands for a materialization that is not only scalable, but also reactive to changes. In this paper, we consider the problem of incremental materialization, that is, how to update the materialized derivations when new data is added or removed. To this purpose, we consider the i¾?df RDFS fragment [12], and present a parallel system that implements a number of algorithms to quickly recalculate the derivation. In case new data is added, our system uses a parallel version of the well-known semi-naive evaluation of Datalog. In case of removals, we have implemented two algorithms, one based on previous theoretical work, and another one that is more efficient since it does not require a complete scan of the input. We have evaluated the performance using a prototype system called DynamiTE, which organizes the knowledge bases with a number of indices to facilitate the query process and exploits parallelism to improve the performance. The results show that our methods are indeed capable to recalculate the derivation in a short time, opening the door to reasoning on much more dynamic data than is currently possible.

A task- and data-parallel programming language based on shared objects

Many programming languages support either task parallelism, but few languages provide a uniform framework for writing applications that need both types of parallelism or data parallelism. We present a programming language and system that integrates task and data parallelism using shared objects. Shared objects may be stored on one processor or may be replicated. Objects may also be partitioned and distributed on several processors.Task parallelism is achieved by forking processes remotely and have them communicate and synchronize through objects. Data parallelism is achieved by executing operations on partitioned objects in parallel. Writing task-and data-parallel applications with shared objects has several advantages. Programmers use the objects as if they were stored in a memory common to all processors. On distributed-memory machines, if objects are remote, replicated, or partitioned, the system takes care of many low-level details such as data transfers and consistency semantics. In this article, we show how to write task-and data-parallel programs with our shared object model. We also desribe a portable implementation of the model. To assess the performance of the system, we wrote several applications that use task and data parallelism and excuted them on a collection of Pentium Pros connected by Myrinet. The performance of these applications is also discussed in this article.

Real-World Distributed Computer with Ibis

The use of parallel and distributed computing systems is essential to meet the ever-increasing computational demands of many scientific and industrial applications. Ibis allows easy programming and deployment of compute-intensive distributed applications, even for dynamic, faulty, and heterogeneous environments.

Source-level global optimizations for fine-grain distributed shared memory systems

This paper describes and evaluates the use of aggressive static analysis in Jackal, a fine-grain Distributed Shared Memory (DSM) system for Java. Jackal uses an optimizing, source-level compiler rather than the binary rewriting techniques employed by most other fine-grain DSM systems. Source-level analysis makes existing access-check optimizations (e.g., access-check batching) more effective and enables two novel fine-grain DSM optimizations: object-graph aggregation and automatic computation migration. The compiler detects situations where an access to a root object is followed by accesses to subobjects. Jackal attempts to aggregate all access checks on objects in such object graphs into a single check on the graphs root object. If this check fails, the entire graph is fetched. Object-graph aggregation can reduce the number of network roundtrips and, since it is an advanced form of access-check batching, improves sequential performance. Computation migration (or function shipping) is used to optimize critical sections in which a single processor owns both the shared data that is accessed and the lock that protects the data. It is usually more efficient to execute such critical sections on the processor that holds the lock and the data than to incur multiple roundtrips for acquiring the lock, fetching the data, writing the data back, and releasing the lock. Jackals compiler detects such critical sections and optimizes them by generating single-roundtrip computation-migration code rather than standard data-shipping code. Jackals optimizations improve both sequential and parallel application performance. On average, sequential execution times of instrumented, optimized programs are within 10% of those of uninstrumented programs. Application speedups usually improve significantly and several Jackal applications perform as well as hand-optimized message-passing programs.

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability and issues including data distribution, software heterogeneity, and ad hoc hardware availability commonly force scientists into simultaneous use of multiple platforms (e.g., clusters, grids, and clouds used concurrently). A true computing jungle .