Paraskevas Evripidou

MPI-FT: PORTABLE FAULT TOLERANCE SCHEME FOR MPI

In this paper, we propose the design and development of a fault tolerant and recovery scheme for the Message Passing Interface (MPI). The proposed scheme consists of a detection mechanism for detec...

Data-Driven Multithreading Using Conventional Microprocessors

This paper describes the data-driven multithreading (DDM) model and how it may be implemented using off-the-shelf microprocessors. Data-driven multithreading is a nonblocking multithreading execution model that tolerates internode latency by scheduling threads for execution based on data availability. Scheduling based on data availability can be used to exploit cache management policies that reduce significantly cache misses. Such policies include firing a thread for execution only if its data is already placed in the cache. We call this cache management policy the CacheFlow policy. The core of the DDM implementation presented is a memory mapped hardware module that is attached directly to the processors bus. This module is responsible for thread scheduling and is known as the thread synchronization unit (TSU). The evaluation of DDM was performed using simulation of the data-driven network of workstations (D2NOW). D2NOW is a DDM implementation built out of regular workstations augmented with the TSU. The simulation was performed for nine scientific applications, seven of which belong to the SPLASH-2 suite. The results show that DDM can tolerate well both the communication and synchronization latency. Overall, for 16 and 32-node D2NOW machines the speedup observed was 14.4 and 26.0, respectively

Mobile Agents for Wireless Computing: The Convergence of Wireless Computational Models with Mobile-Agent Technologies

Wireless mobile computing breaks the stationary barrier and allows users to compute and access information from anywhere and at anytime. However, this new freedom of movement does not come without new challenges. The mobile computing environment is constrained in many ways. Mobile elements are resource-poor and unreliable. Their network connectivity is often achieved through low-bandwidth wireless links. Furthermore, connectivity is frequently lost for variant periods of time. The difficulties raised by these constraints are compounded by mobility that induces variability in the availability of both communication and computational resources. These severe restrictions have a great impact on the design and structure of mobile computing applications and motivate the development of new software models. To this end, a number of extensions to the traditional distributed system architectures have been proposed [26]. These new software models, however, are static and require a priori set up and configuration. This in effect limits their potential in dynamically serving the mobile client; the client cannot access a site at which an appropriate model is not configured in advance. The contribution of this paper is twofold. First, the paper shows how an implementation of the proposed models using mobile agents eliminates this limitation and enhances the utilization of the models. Second, new frameworks for Web-based distributed access to databases are proposed and implemented.

TERAFLUX: Harnessing dataflow in next generation teradevices

The improvements in semiconductor technologies are gradually enabling extreme-scale systems such as teradevices (i.e., chips composed by 1000 billion of transistors), most likely by 2020. Three major challenges have been identified: programmability, manageable architecture design, and reliability. TERAFLUX is a Future and Emerging Technology (FET) large-scale project funded by the European Union, which addresses such challenges at once by leveraging the dataflow principles. This paper presents an overview of the research carried out by the TERAFLUX partners and some preliminary results. Our platform comprises 1000+ general purpose cores per chip in order to properly explore the above challenges. An architectural template has been proposed and applications have been ported to the platform. Programming models, compilation tools, and reliability techniques have been developed. The evaluation is carried out by leveraging on modifications of the HP-Labs COTSon simulator.

TFlux: A Portable Platform for Data-Driven Multithreading on Commodity Multicore Systems

In this paper we present thread flux (TFlux), a complete system that supports the data-driven multithreading (DDM) model of execution. TFlux virtualizes any details of the underlying system therefore offering the same programming model independently of the architecture. To achieve this goal, TFlux has a runtime support that is built on top of a commodity operating system. Scheduling of threads is performed by the thread synchronization unit (TSU), which can be implemented either as a hardware or a software module. In addition, TFlux includes a preprocessor that, along with a set of simple compiler directives, allows the user to easily develop DDM programs. The preprocessor then automatically produces the TFlux code, which can be compiled using any commodity C compiler, therefore automatically producing code to any ISA. TFlux has been validated on three platforms. A Simics-based multicore system with a TSU hardware module (TFluxHard), a commodity 8-core Intel Core2 QuadCore-based system with a software TSU module (TFluxSoft), and a Cell/BE system with a software TSU module (TFluxCell). The experimental results show that the performance achieved is close to linear speedup, on average 21x for the 27 nodes TFluxHard, and 4.4x on a 6 nodes TFluxSoft and TFluxCell. Most importantly, the observed speedup is stable across the different platforms thus allowing the benefits of DDM to be exploited on different commodity systems.

A relationally complete visual query language for heterogeneous data sources and pervasive querying

In this paper we introduce and formally define Query by Browsing (QBB), a scalable, relationally complete visual query language based on the desktop user interface paradigm and tuple relational calculus that allows the formulation of complex queries over relational, entity-relationship, object-oriented and XML data sources on a variety of handheld and desktop platforms. It is to our knowledge the first visual query language to combine the important characteristics of usability, scalability, expressive power and flexibility. We support these claims by demonstrating the similarity of the QBB paradigm to the popular desktop user interface paradigm, by relating it to relational calculus and relational algebra and by describing Chiromancer II, a Web-based implementation of the QBB paradigm for handheld devices. We also discuss ways in which non-relational sources can be represented and queried and compare QBB to related work in the area of visual query languages for a variety of data models. We finally offer conclusions and thoughts for future work.

The TERAFLUX Project: Exploiting the DataFlow Paradigm in Next Generation Teradevices

Thanks to the improvements in semiconductor technologies, extreme-scale systems such as teradevices (i.e., composed by 1000 billion of transistors) will enable systems with 1000+ general purpose cores per chip, probably by 2020. Three major challenges have been identified: programmability, manageable architecture design, and reliability. TERAFLUX is a Future and Emerging Technology (FET) large-scale project funded by the European Union, which addresses such challenges at once by leveraging the dataflow principles. This paper describes the project and provides an overview of the research carried out by the TERAFLUX consortium.

Advanced array optimizations for high performance functional languages

We discuss and evaluate three optimizations for reducing memory management overhead and data copying costs in SISAL 1.2 programs that build arrays. The first, called framework preconstruction, eliminates superfluous allocate-deallocate sequences in cyclic computations. The second, called aggregate storage subsumption, reduces the management overhead for compound array components. The third, called predictive storage preallocation, eliminates superfluous data copying in filtered array constructions and simplifies their parallelization. We have added all three optimizations to the Optimizing SISAL Compiler with rewarding improvements in SISAL program performance on vector-parallel machines such as those built by Cray Computer Corporation, Convex, and Cray Research. >

The PaCMAn metacomputer: parallel computing with Java mobile agents

Abstract The PaCMAn (parallel computing with Java mobile agents) Metacomputer launches multiple Java mobile agents that communicate and cooperate to solve problems in parallel. Each mobile agent can travel anywhere in the Web to perform its tasks. A number of brokers/load forecasters keep track of the available resources and provide load forecast to the clients. The clients select the servers that they will utilize based on the specific resource requirements and the load forecast. The PaCMAn mobile agents are modular; the mobile shell is separated from the specific task code of the target application. To this end, we introduce the concept of TaskHandlers which are Java objects capable of implementing a particular task of the target application. TaskHandlers are dynamically assigned to PaCMAn’s mobile agents. We have developed and tested a prototype system with several applications such as parallel Web querying, a prime number generator, the trapezoidal rule and the RC5 cracking application. Our results demonstrate that PaCMAn provide very good parallel efficiency.

CacheFlow: A short-term optimal cache management policy for Data Driven Multithreading

With Data Driven Multithreading a thread is scheduled for execution only if all of its inputs have been produced and placed in the processor’s local memory. Scheduling based on data availability may be used to exploit short-term optimal cache management policies. Such policies include firing a thread for execution only if its code and data are already placed in the cache. Furthermore, blocks associated to threads scheduled for execution in the near future, are not replaced until the thread starts its execution. We call this short-term optimal cache management policy the CacheFlow policy.