7983044
Algorithm-based checkpoint-free fault tolerance for parallel matrix computations on volatile resources
As the size of today's high performance computers increases from hundreds, to thousands, and even tens of thousands of processors, node failures in these computers are becoming frequent events. Although checkpoint/rollback-recovery is the typical technique to tolerate such failures, it often introduces a considerable overhead. Algorithm-based fault tolerance is a very cost-effective method to incorporate fault tolerance into matrix computations. However, previous algorithm-based fault tolerance methods for matrix computations are often derived using algorithms that are seldomly used in the practice of today's high performance matrix computations and have mostly focused on platforms where failed processors produce incorrect calculations. To fill this gap, this paper extends the existing algorithm-based fault tolerance to the volatile computing platform where the failed processor stops working and applies it to scalable high performance matrix computations with two dimensional block cyclic data distribution. We show the practicality of this technique by applying it to the ScaLAPACK/PBLAS matrix-matrix multiplication kernel. Experimental results demonstrate that the proposed approach is able to survive process failures with a very low performance overhead
1853937
Numerically Stable Real Number Codes Based on Random Matrices
Error correction codes defined over real-number field have been studied and recognized as useful in many applications. However, most real-number codes in literature are quite suspect in their numerical stability. In this paper, we introduce a class of real-number codes based on random generator matrices over real-number fields. Codes over complex-number field are also discussed. Experiment results demonstrate our codes are numerically much more stable than existing codes in literature.
1479535
Self Adaptive Application Level Fault Tolerance for Parallel and Distributed Computing
Most application level fault tolerance schemes in literature are non-adaptive in the sense that the fault tolerance schemes incorporated in applications are usually designed without incorporating information from system environments such as the amount of available memory and the local or network I/O bandwidth. However, from an application point of view, it is often desirable for fault tolerant high performance applications to be able to achieve high performance under whatever system environment it executes with as low fault tolerance overhead as possible In this paper, we demonstrate that, in order to achieve high reliability with as low performance penalty as possible, fault tolerant schemes in applications need to be able to adapt themselves to different system environments. We propose a framework under which different fault tolerant schemes can be incorporated in applications using an adaptive method. Under this framework, applications are able to choose near optimal fault tolerance schemes at run time according to the specific characteristics of the platform on which the application is executing.
5809396
Self-adapting numerical software (SANS) effort
The challenge for the development of next-generation software is the successful management of the complex computational environment while delivering to the scientist the full power of flexible compositions of the available algorithmic alternatives. Self-adapting numerical software (SANS) systems are intended to meet this significant challenge. The process of arriving at an efficient numerical solution of problems in computational science involves numerous decisions by a numerical expert. Attempts to automate such decisions distinguish three levels: algorithmic decision, management of the parallel environment, and processor-specific tuning of kernels. Additionally, at any of these levels we can decide to rearrange the user's data. In this paper we look at a number of efforts at the University of Tennessee to investigate these areas.
7363283
Self-adapting software for numerical linear algebra and LAPACK for clusters
This article describes the context, design, and recent development of the LAPACK for clusters (LFC) project. It has been developed in the framework of Self-Adapting Numerical Software (SANS) since we believe such an approach can deliver the convenience and ease of use of existing sequential environments bundled with the power and versatility of highly tuned parallel codes that execute on clusters. Accomplishing this task is far from trivial as we argue in the paper by presenting pertinent case studies and possible usage scenarios.
8890999
A unified framework for clustering heterogeneous Web objects
We introduce a novel framework for clustering Web data which is often heterogeneous in nature. As most existing methods often integrate heterogeneous data into a unified feature space, their flexibilities to explore and adjust contributing effects from different heterogeneous information are compromised. In contrast, our framework enables separate clustering of homogeneous data in the entire process based on their respective features, and a layered structure with link information is used to iteratively project and propagate the clustered results between layers until it converges. Our experimental results show that such a scheme not only effectively overcomes the problem of data sparseness caused by the high dimensional link space but also improves the clustering accuracy significantly. We achieve 19% and 41% performance increases when clustering Web-pages and users based on a semi-synthetic Web log. Finally, we show a real clustering result based on UC Berkeley's Web log.
6289519
Second-level buffer cache management
Buffer caches are commonly used in servers to reduce the number of slow disk accesses or network messages. These buffer caches form a multilevel buffer cache hierarchy. In such a hierarchy, second-level buffer caches have different access patterns from first-level buffer caches because accesses to a second-level are actually misses from a first-level. Therefore, commonly used cache management algorithms such as the least recently used (LRU) replacement algorithm that work well for single-level buffer caches may not work well for second-level. We investigate multiple approaches to effectively manage second-level buffer caches. In particular, we report our research results in 1) second-level buffer cache access pattern characterization, 2) a new local algorithm called multi-queue (MQ) that performs better than nine tested alternative algorithms for second-level buffer caches, 3) a set of global algorithms that manage a multilevel buffer cache hierarchy globally and significantly improve second-level buffer cache hit ratios over corresponding local algorithms, and 4) implementation and evaluation of these algorithms in a real storage system connected with commercial database servers (Microsoft SQL server and Oracle) running industrial-strength online transaction processing benchmarks.
23589102
Bounds of the longest directed cycle length for minimal strong digraphs
Abstract In this paper we present the upper and lower bounds of the longest directed cycle length for minimal strong digraphs in terms of the numbers of vertices and arcs. These bounds are both sharp. In addition, we give analogous results for minimal 2-edge connected graphs.
9213286
Modeling the spread of active worms
Active worms spread in an automated fashion and can flood the Internet in a very short time. Modeling the spread of active worms can help us understand how active worms spread, and how we can monitor and defend against the propagation of worms effectively. In this paper, we present a mathematical model, referred to as the Analytical Active Worm Propagation (AAWP) model, which characterizes the propagation of worms that employ random scanning. We compare our model with the Epidemiological model and Weaver's simulator. Our results show that our model can characterize the spread of worms effectively. Taking the Code Red v2 worm as an example, we give a quantitative analysis for monitoring, detecting and defending against worms. Furthermore, we extend our AAWP model to understand the spread of worms that employ local subnet scanning. To the best of our knowledge, there is no model for the spread of a worm that employs the localized scanning strategy and we believe that this is the first attempt on understanding local subnet scanning quantitatively.
432736
ReCoM: reinforcement clustering of multi-type interrelated data objects
Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.