Shahnawaz Talpur

Exploiting controlled-grained parallelism in message-driven stream programs

With the increasing amount of parallelism obtainable on multicore platforms, stream programming has been proposed as an effective solution for exposing distributed parallelization. Nonetheless, a pressing demand of scheduling task and data parallelism in stream programming exists that can accomplish robust multicore performance in the face of varying application characteristics. This paper addresses the problem of scheduling task and data parallelism in stream programming. We present StreamMDE, an asynchronous concurrency stream programming framework which offers a novel parallel programming model for scheduling task and data parallelism in the message-driven execution paradigm. A key property of this framework is exposing controlled-grained parallelism, which allows us to control the granularity of task and data parallelism in stream graph. Our empirical evaluation of StreamMDE shows that higher efficiency of mixed task and data parallelism in stream programming can be exploited with the appropriate granularity control. The framework bridges the gap between the parallel scale and the architecture of stream programs and facilitates in designing and coding stream features in different schedules.

A Novel Curvature Feature Embedded Level Set Method for Image Segmentation of Coronary Angiograms

Segmentation methods in medical image processing are usually distorted by low contrast and intensity inhomogeneity. There are several image segmentation methods which are based on region based segmentation. But these algorithms mostly depend on the quality of the image. This paper gives an improved level set method for image segmentation to reduce the effect of noise. In order to achieve this, curvature feature energy function in standard level set energy function has been used. The proposed method is being applied on heart angiograms provided by Cardiac Department ISRA University Hospital, Pakistan. Extensive evaluation of these images depicts the robustness and efficiency of the proposed method over the previous work. Moreover, this method gives better trade-off between accuracy and implementation time over the related work.

Exploring grouped coherence for clustered hierarchical cache

The industry trends for processors are toward integrating an increasing number of cores into a single chip. Researchers have to deal with frequent data migration across network-on-chip and the increasing on-chip traffic. The innovation from flat to hierarchy is probably a natural design methodology for scalable systems (Martin et al. in Commun ACM, 55(7):78–89, 2012. doi:10.1145/2209249.2209269). Unfortunately, the alternative of hierarchical directory protocol inevitably leads to on-chip traffic overhead, protocol complexity and access latency. In this paper, we target hierarchical cache coherence protocol to overcome the potentially high cost of maintaining cache coherence in current multicore processors. We propose a novel vertical caching protocol combined with grouped coherence, in which the coherence domain expand on demand. More specifically, its design philosophy is to provide a ‘best-effort’ single-copy delivery which allows the shared data only in the first common shared level. Compared to the previous hierarchical protocol, our proposal is able to achieve the performance improvement of 9.9% in the 16-core system and 13.4% in the 64-core system as well as an on-chip traffic reduction of about 10.8% in the 16-core system and 15.9% in the 64-core system, respectively.

Achieving self-aware parallelism in stream programs

The age of big data open the door to a new approach in data exploration and utilization. With the increasing complexities and dynamics of modern IT systems and services, it has become a challenge to effectively exploit parallelism on multicore platforms in computing systems that are heterogeneous, dynamic and decentralised. Self-aware software is a response to these demands in dealing with distributed applications in changing environments. It is a closed-loop system with a series of optimization strategies to adjust itself dynamicly during data processing. We focus on incorporating adaptation mechanisms into the stream programs for exposing distributed parallelism. In the traditional stream programming models, changing data and status normally require human supervision to adjust the stream graph for performance. As one-time optimization strategy, the reconfiguration and maintenance lead to costly and time-consuming procedures during the operating phase. To address these problems, we propose a self-aware stream programming model called StreamAware. A key property of this model is that exposing self-aware parallelism in the message driven execution paradigm, which provides dynamic and reconfigurable stream graph in adapting to the data flow changes. The model defines the self-awareness loop based on finite state machine for stream applications to adjust their own stream graph with continuous optimization strategy. This paper presents three different self-aware systems built using StreamAware. The empirical evaluation demonstrate how these systems can exploit self-aware parallelism using the Parsec benchmark problems, optimize performance per Watt, and respond to significant changes in stream processing.