Abstract

In extant quantum secret sharing protocols, once the secret is shared in a quantum network (qnet) it cannot be retrieved, even if the dealer wishes that his/her secret no longer be available in the network. For instance, if the dealer is part of two qnets, say Q1 and Q2 and he/she subsequently finds that Q2 is more reliable than Q1, he/she may wish to transfer all her secrets from Q1 to Q2. Known protocols are inadequate to address such a revocation. In this work we address this problem by designing a protocol that enables the source/dealer to bring back the information shared in the network, if desired. Unlike classical revocation, the no-cloning theorem automatically ensures that the secret is no longer shared in the network.

Abstract

Integrated software products are complex in design. They are prone to defects caused by integrated and non-integrated modules of the entire integrated software suite. In such software products, a small proportion of defects are fixed as soon as they are reported. Rest of the defects are targeted for fixes in future product release cycles. Among such targeted defects, most of them seem to be insignificant and innocuous in the current version but have the potential to become acute in future versions. In this paper, we propose an approach to study defect dependency of the reported defect using a dependency metric. Identifying the dependency of a defect in an integrated product suite can help the product stake-owners to prioritize them and help improve software quality.

Abstract

Multiplying two sparse matrices, denoted spmm, is a fundamental operation in linear algebra with several applications. Hence, efficient and scalable implementation of spmm has been a topic of immense research. Recent efforts are aimed at implementations on GPUs, multicore architectures, FPGAs, and such emerging computational platforms. Owing to the highly irregular nature of spmm, it is observed that GPUs and CPUs can offer comparable performance. In this paper, we study CPU+GPU heterogeneous algorithms for spmm where the matrices exhibit a scale-free nature. Focusing on such matrices, we propose an algorithm that multiplies two sparse matrices exhibiting scale-free nature on a CPU+GPU heterogeneous platform. Our experiments on a wide variety of real-world matrices from standard datasets show an average of 25% improvement over the best possible algorithm on a CPU+GPU heterogeneous platform. We show that our approach is both architecture-aware, and workload-aware.

Abstract

India has 790+ million active mobile connections and 80.57 million smartphone users. However, as per Reserve Bank of India, the number of transactions performed using smartphone based mobile banking applicationsis less than 12% of the overall banking transactions. One of the major reasons for such low numbers is the usability of the mobile banking app. In this paper, we focus on usability issues related tomobile banking apps and propose a Mobile App Usability Index (MAUI) for enhancing the usability of a mobile banking app. The proposed Index has been validatedwith mobile banking channel managers, chief information security officers, etc.

Abstract

In this work we show that given a set S of n points with coordinates on an n × n grid, we can construct data structures for (i) reporting and (ii) counting the maximal points in an axes-parallel query rectangle in sub-logarithmic time. We assume our model of computation to be the word RAM with size of each word being log n bits.

Abstract

Aspect oriented programming (AOP) is a programming paradigm that provides mechanisms for encapsulating cross-cutting concerns. Developers who want to learn AOP often experiment with aspects and different Pointcuts on their existing code bases to try and incorporate aspects into their software project. This typically involves an iterative process of testing multiple Pointcuts, partially due to the new syntax the developer has to learn, as well as to try and find the aspectual implementation that best fits their needs. Incremental weaving time is a huge factor in making this process smooth and faster for developers to more readily learn and adopt AOP. We propose an optimization approach that focuses on reducing redundant re-compilations and weaving. We propose that the approach can reduce the time being spent for weaving and thereby facilitating faster iterative project build time. We have done some initial

Abstract

Refactoring is a set of techniques used to enhance the quality of code by restructuring existing code/design without changing its behavior. Refactoring tools can be used to detect specific code smells, propose relevant refactorings, and in some cases automate the refactoring process. However, usage of refactoring tools in industry is still relatively low. One of the major reasons being the veracity of the detected code smells, especially smells that aren’t purely syntactic in nature. We conduct an empirical study on some refactoring tools and evaluate the correctness of the code smells they identify. We analyze the level of confidence users have on the code smells detected by the tools and discuss some issues with such tools.

Abstract

Product based organizations have diverse product offerings that meet various business needs. The products are in turn integrated to create integrated product suites. Rigorous product engineering is a must for creation of high quality integrated software products. Adequate measures must be taken to improve quality of the integrated product before every release of its module or sub-product. It is hard to imagine upgrading an integrated software product with unidentified defects prior to its release. In this paper, we share our observations on implementing a defect dependency metric to identify the dependency of a defect over a real-time industry defect dataset of an integrated software product. This defect dependency metric was captured and analyzed during release cycle (s) to avoid surprise issues post product launch.

Abstract

Motivated by the explosion of Big Data analytics, performance improvements in low-power (wimpy) systems and the increasing energy efficiency of GPUs, this paper presents a time–energy performance analysis of MapReduce on heterogeneous systems with GPUs. We evaluate the time and energy performance of three MapReduce applications with diverse resource demands on a Hadoop–CUDA framework. As executing these applications on heterogeneous systems with GPUs is challenging, we introduce a novel lazy processing technique which requires no modifications to the underlying Hadoop framework. To analyze the impact of heterogeneity, we compare the heterogeneous CPU+GPU with the homogeneous CPU-only execution across three systems with diverse characteristics, (i) a traditional high-performance (brawny) Intel i7 system hosting a discrete 640-core Nvidia GPU of the latest Maxwell generation, (ii) a wimpy platform consisting of a quad-core ARM Cortex-A9 hosting the same discrete Maxwell GPU, and (iii) a wimpy platform integrating four ARM Cortex-A15 cores and 192 Nvidia Kepler GPU cores on the same chip. These systems encompass both intra-node heterogeneity with discrete GPUs and intra-chip heterogeneity with integrated GPUs. Our measurement-based performance analysis highlights the following results. For compute-intensive workloads, the brawny heterogeneous system achieves speedups of up to 2.3 and reduces the energy usage by almost half compared to the brawny homogeneous system. As expected, for applications where data transfers dominate the execution time, heterogeneity exhibits worse time–energy performance compared to homogeneous systems. For such applications, the heterogeneous wimpy A9 system with discrete GPU uses around 14 times the energy of homogeneous A9 system due to both system resource imbalances and high power overhead of the discrete GPU. However, comparing among heterogeneous systems, the wimpy A15 with integrated GPU uses the lowest energy across all workloads. This allows us to establish an execution time equivalence ratio between a single brawny node and multiple wimpy nodes. Based on this equivalence ratio, the wimpy nodes exhibit energy savings of two-thirds while maintaining the same execution time. This result advocates the potential usage of heterogeneous wimpy systems with integrated GPUs for Big Data analytics.

Abstract

Hybrid programming model is becoming increasingly popular for HPC applications as it has the dual-advantage of exploiting inter-node distributed-memory scalability and intra-node shared-memory performance in a cluster system. One of the key challenges for energy efficient execution of hybrid programs is to determine time and energy efficient hardware configurations among a large system configuration space. Given a hybrid program with an execution time deadline and an energy budget, we propose a measurement-based analytical modelling approach to determine these system configurations. In contrast to current approaches, we model both inter and intra-node resource overlaps, memory contention among cores within a node and network contention across multiple nodes. The model invalidated against direct measurement using five representative HPC applications on Intel Xeon and ARM clusters having diverse time-energy performance. We show that a Pareto frontier consisting of optimal configurations exist for a hybrid program running on homogeneous clusters. To further optimize the Pareto frontier, we introduce a new metric, useful computation ratio (UCR) to quantify the degree of resource contentions and communication overheads in an execution. We discuss how UCR and Pareto-optimal configurations can be used in conjunction by system's designers to gain further insights into system resource imbalances, and how application developers can further fine-tune their hybrid programs