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Haitao Wei

Bio: Haitao Wei is an academic researcher from University of Delaware. The author has contributed to research in topics: Dataflow & Programming paradigm. The author has an hindex of 2, co-authored 3 publications receiving 19 citations.

Papers
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Journal ArticleDOI
01 Jan 2014
TL;DR: COStream, a programming language based on synchronous data flow execution model for data-driven application, is proposed and a compiler framework for COStream is proposed on general-purpose multi-core architectures.
Abstract: The dataflow programming paradigm shows an important way to improve programming productivity for streaming systems. In this paper we propose COStream, a programming language based on synchronous data flow execution model for data-driven application. We also propose a compiler framework for COStream on general-purpose multi-core architectures. It features an inter-thread software pipelining scheduler to exploit the parallelism among the cores. We implemented the COStream compiler framework on x86 multi-core architecture and performed experiments to evaluate the system.

11 citations

Journal ArticleDOI
01 Jan 2015
TL;DR: This paper presents Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges.
Abstract: The DDDAS paradigm, unifying applications, mathematical modeling, and sensors, is now more relevant than ever with the advent of Large-Scale/Big-Data and Big-Computing. Large-Scale-Dynamic-Data (advertised as the next wave of Big Data) includes the integrated range of data from high-end systems and instruments together with the dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems. In this paper we present Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges. Development of simulation models and a compiler for Fresh Breeze computer systems is discussed and initial results are reported.

8 citations

Proceedings ArticleDOI
24 Aug 2014
TL;DR: This work proposes a cross-cutting cross-layer approach to address the challenges posed by future heterogeneous many-core chips, and suggests that data locality, already a must-have in high-performance computing, will become even more critical as memory technology progresses.
Abstract: Computer systems have undergone a fundamental transformation recently, from single-core processors to devices with increasingly higher core counts within a single chip. The semi-conductor industry now faces the infamous power and utilization walls. To meet these challenges, heterogeneity in design, both at the architecture and technology levels, will be the prevailing approach for energy efficient computing as specialized cores, accelerators, etc., can eliminate the energy overheads of general-purpose homogeneous cores. However, with future technological challenges pointing in the direction of on-chip heterogeneity, and because of the traditional difficulty of parallel programming, it becomes imperative to produce new system software stacks that can take advantage of the heterogeneous hardware. As a case in point, the core count per chip continues to increase dramatically while the available on-chip memory per core is only getting marginally bigger. Thus, data locality, already a must-have in high-performance computing, will become even more critical as memory technology progresses. In turn, this makes it crucial that new execution models be developed to better exploit the trends of future heterogeneous computing in many-core chips. To solve these issues, we propose a cross-cutting cross-layer approach to address the challenges posed by future heterogeneous many-core chips.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: This tool simplifies the transformation of sequential source code to parallel and provides support for identifying Map, Farm, and Pipeline parallel patterns and evaluates the quality of the detection for a set of different C++ applications.
Abstract: Since the ‘free lunch’ of processor performance is over, parallelism has become the new trend in hardware and architecture design. However, parallel resources deployed in data centers are underused in many cases, given that sequential programming is still deeply rooted in current software development. To address this problem, new methodologies and techniques for parallel programming have been progressively developed. For instance, parallel frameworks, offering programming patterns, allow expressing concurrency in applications to better exploit parallel hardware. Nevertheless, a large portion of production software, from a broad range of scientific and industrial areas, is still developed sequentially. Considering that these software modules contain thousands, or even millions, of lines of code, an extremely large amount of effort is needed to identify parallel regions. To pave the way in this area, this paper presents Parallel Pattern Analyzer Tool, a software component that aids the discovery and annotat...

14 citations

Proceedings ArticleDOI
05 Jun 2017
TL;DR: This paper makes the preliminary attempt to develop the dataflow insight into a specialized graph accelerator and believes that this work would open a wide range of opportunities to improve the performance of computation and memory access for large-scale graph processing.
Abstract: Existing graph processing frameworks greatly improve the performance of memory subsystem, but they are still subject to the underlying modern processor, resulting in the potential inefficiencies for graph processing in the sense of low instruction level parallelism and high branch misprediction. These inefficiencies, in accordance with our comprehensive micro-architectural study, mainly arise out of a wealth of dependencies, serial semantic of instruction streams, and complex conditional instructions in graph processing. In this paper, we propose that a fundamental shift of approach is necessary to break through the inefficiencies of the underlying processor via the dataflow paradigm. It is verified that the idea of applying dataflow approach into graph processing is extremely appealing for the following two reasons. First, as the execution and retirement of instructions only depend on the availability of input data in dataflow model, a high degree of parallelism can be therefore provided to relax the heavy dependency and serial semantic. Second, dataflow is guaranteed to make it possible to reduce the costs of branch misprediction by simultaneously executing all branches of a conditional instruction. Consequently, we make the preliminary attempt to develop the dataflow insight into a specialized graph accelerator. We believe that our work would open a wide range of opportunities to improve the performance of computation and memory access for large-scale graph processing.

8 citations

Journal ArticleDOI
01 Jan 2015
TL;DR: This paper presents Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges.
Abstract: The DDDAS paradigm, unifying applications, mathematical modeling, and sensors, is now more relevant than ever with the advent of Large-Scale/Big-Data and Big-Computing. Large-Scale-Dynamic-Data (advertised as the next wave of Big Data) includes the integrated range of data from high-end systems and instruments together with the dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems. In this paper we present Fresh Breeze, a dataflow-based execution and programming model and computer architecture and how it provides a sound basis to develop future computing systems that match the DDDAS challenges. Development of simulation models and a compiler for Fresh Breeze computer systems is discussed and initial results are reported.

8 citations

Proceedings ArticleDOI
10 Jul 2018
TL;DR: Experimental results demonstrate the DDDAS-based Live Video Computing DataBase Modeling Systems (LVC-DBMS) approach affording data discovery and query-based flexibility for awareness to provide narratives of unknown situations.
Abstract: With ubiquitous data acquired from sensors, there is an ever increasing ability to abstract content from the combination of physics-based and human-derived information fusion (PHIF) The advancement of PHIF tools include graphical information fusion methods, target tracking techniques, and natural language understanding Current discussions revolve around dynamic data-driven applications systems (DDDAS) which seeks to leverage high-dimensional modeling with real-time physical systems An example of a model includes a learned dictionary that can be leveraged as information queries In this paper, we discuss the DDDAS paradigm of sensor measurements, information processing, environmental modeling, and software implementation to deliver content for PHIF systems Experimental results demonstrate the DDDAS-based Live Video Computing DataBase Modeling Systems (LVC-DBMS) approach affording data discovery and query-based flexibility for awareness to provide narratives of unknown situations

7 citations