Abstract

Parallel graph algorithms continue to attract a lot of research attention given their applications to several fields of sciences and engineering. Efficient design and implementation of graph algorithms on modern manycore accelerators has to however contend with a host of challenges including not being able to reach full memory system throughput and irregularity. Of late, focusing on real-world graphs, researchers are addressing these challenges by using decomposition and preprocessing techniques guided by the structural properties of such graphs. In this direction, we present a new GPU algorithm for obtaining an ear decomposition of a graph. Our implementation of the proposed algorithm on an NVidia Tesla K40c improves the state-of-the-art by a factor of 2.3x on average on a collection of real-world and synthetic graphs. The improved performance of our algorithm is due to our proposed characterization that identifies edges of the graph as redundant for the purposes of an ear decomposition. We then study an application of the ear decomposition of a graph in computing the betweenness-centrality values of nodes in the graph. We use an ear decomposition of the input graph to systematically remove nodes of degree two. The actual computation of betweenness-centrality is done on the remaining nodes and the results are extended to nodes removed in the previous step. We show that this approach improves the state-of-the-art for computing betweenness-centrality on an NVidia K40c GPU by a factor of 1.9x on average over a collection of real-world graphs.

Abstract

This paper extends recent work on vulnerability extrapolation. A surge in vulnerability exploits against old and new softwares, urges the importance of detection of vulnerabilities and possible attacks prior to the attacker. How sophisticated an exploit may be, an underlying prerequisite remains to be the presence of at least one memory corruption bug, serving as entry point for the exploit. Therefore several rigorous software testing techniques are borrowed to detect and eliminate software bugs as early as possible. Code similarity based bug detection is one of such techniques, which, in the parlance of software security, is also termed as vulnerability extrapolation. In this paper, we present a source code similarity based bug identification technique by considering code features that are relevant for security related bugs. Our technique works by enriching (augmenting) abstract syntax trees (ASTs) of functions by considering security relevant properties of the code. We show the effectiveness of the augmented AST based similarity approach over existing methods by evaluating proposed method on real-world applications.

Abstract

Consider a synchronous distributed network which is partly controlled by an adversary. In a Perfectly Secret Message Transmission(PSMT) protocol, the sender S wishes to transmit a message to the receiver R such that the adversary learns nothing about the message. We characterize the set of directed graphs that admit PSMT protocols tolerating a dual failure model where up to tp nodes are passively corrupted and further up to any tf nodes may fail.

Abstract

Software quality has always been an important criterion for assessing stability of a product. It is quite challenging for large-scale complex products, especially integrated products, to endure and withstand the competition after a new version release in its market domain. Unlike regular software, integrated software products require detailed exploration on the spread and impact of a defect to improve overall product quality. In this paper, we use heuristic approaches like generalized defect dependency approach, control flow graph based approach, and feature correlation based approach to study the widespread of defects in large software and suggest a metric called defect dependency metric to study the dependency of defects. We implemented the generalized defect dependency approach on an industry dataset and gathered noteworthy results. We provide a comparative a study of the heuristic approaches …

Abstract

Software engineering activities like code reviews, change management, knowledge management, issue tracking, etc. tend to be heavily process oriented. Gamification of such activities by composing the core activities with game design elements like badges and points can increase developers' interest in performing such activities. While there are various frameworks/applications that assist in gamification, extending the frameworks to add any/all desired game design elements has not been adequately addressed. In this paper, we propose an extensible architectural framework for gamification of software engineering activities where in the game design elements are modeled as services. We create an example instance of our framework by building a prototype for code review activity and note the challenges of designing such an extensible architectural framework. The example instance uses python's Flask micro …

Abstract

Energy efficiency is an area of growing importance in datacenters. While the energy proportionality wall poses a challenge to further improve the dynamic power range of servers, heterogeneous systems offer a new opportunity to achieve higher energy efficiency by improving the match between application workload demands on the large heterogeneous system configuration space. This paper proposes a model-driven energy proportionality analysis of heterogeneous clusters consisting of server nodes with different performance-to-power ratio (PPR). Our analysis shows that inter-node heterogeneity has a positive effect of scaling the energy proportionality wall by exposing configurations with sub-linear energy proportionality. Secondly, analysis of these sub-linear configurations on the 95th percentile response time shows that heterogeneity is beneficial in workloads where the PPR of wimpy nodes is higher than brawny nodes.

Abstract

Many Integrated Core (MIC) architecture systems are becoming increasingly popular for HPC applications as they have the dual-advantage of accelerating vector processing and a general-purpose programming model. One of the key challenges for energy-efficient execution on MIC architecture systems is to determine time and energy-efficient configurations among a large system configuration space. Given a parallel program with an execution time deadline and an energy budget, we propose a measurement-based analytical modeling approach to determine these system configurations. In contrast to current approaches, we model both inter-and intra-core resource overlaps, memory contention among threads within a core and memory contention across multiple cores. The model is validated against direct measurement using six representative HPC applications on Intel Xeon Phi system. We show that a Pareto frontier consisting of optimal configurations exist for a parallel program executing on MIC architecture systems. To further understand the Pareto frontier, we use the performance-to-power ratio metric (PPR), that quantifies the amount of useful computations performed per unit energy during the execution. The proposed approach can be used to determine what thread affinity is suitable for offloading execution to accelerators such as Xeon Phi and save energy.

Abstract

We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis.

Abstract

Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional …

Abstract

Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The post stroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation-inhibition (E-I) balance hypothesis to objectively quantify the post stroke individual brain state using online fNIRS-EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local excitation-inhibition (that is the ratio of Glutamate/GABA) which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons which can be captured with excitation-inhibition based brain models. Furthermore, E-I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing which can then be implicated in changes in function. We first review evidence that shows how this local imbalance between excitation-inhibition leading to functional