Gabor Karsai

Bio: Gabor Karsai is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Software system & Software development. The author has an hindex of 49, co-authored 376 publications receiving 12381 citations. Previous affiliations of Gabor Karsai include Budapest University of Technology and Economics.

Smart Dust: communicating with a cubic-millimeter computer

Abstract: Domain-specific integrated development environments can help capture specifications in the form of domain models. These tools support the design process by automating analysis and simulating essential system behavior. In addition, they can automatically generate, configure, and integrate target application components. The high cost of developing domain-specific, integrated modeling, analysis, and application-generation environments prevents their penetration into narrower engineering fields that have limited user bases. Model-integrated computing (MIC), an approach to model-based engineering that helps compose domain-specific design environments rapidly and cost effectively, is particularly relevant for specialized computer-based systems domains-perhaps even single projects. The authors describe how MIC provides a way to compose such environments cost effectively and rapidly by using a metalevel architecture to specify the domain-specific modeling language and integrity constraints. They also discuss the toolset that implements MIC and describe a practical application in which using the technology in a tool environment for the process industry led to significant reductions in development and maintenance costs.

Software Engineering for Self-Adaptive Systems: A Research Roadmap

Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and to identify critical challenges for the systematic software engineering of self-adaptive systems. The paper is partitioned into four parts, one for each of the identified essential views of self-adaptation: modelling dimensions, requirements, engineering, and assurances. For each view, we present the state-of-the-art and the challenges that our community must address. This roadmap paper is a result of the Dagstuhl Seminar 08031 on "Software Engineering for Self-Adaptive Systems," which took place in January 2008.

Software Engineering for Self-Adaptive Systems : A Second Research Roadmap

Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

The Generic Modeling Environment

Abstract: The Generic Modeling Environment (GME) is a con- figurable toolset that supports the easy creation of d o- main-specific modeling and program synthesis environ- ments. The primarily graphical, domain-specific models can represent the application and its environment includ- ing hardware resources, and their relationship. The mod- els are then used to automatically synthesize the applica- tion and/or generate inputs to COTS analysis tools. In addition to traditional signal processing problems, we have applied this approach to tool integration and struc- turally adaptive systems among other domains. This pa- per describes the GME toolset and compares it to other similar approaches. A case study is also presented that illustrates the core concepts through an example.

Model-integrated computing

Abstract: Computers now control many critical systems in our lives, from the brakes on our cars to the avionics control systems on planes. Such computers wed physical systems to software, tightly integrating the two and generating complex component interactions unknown in earlier systems. Thus, it is imperative that we construct software and its associated physical system so they can evolve together. The paper discusses one approach that accomplishes this called model-integrated computing. This works by extending the scope and use of models. It starts by defining the computational processes that a system must perform and develops models that become the backbone for the development of computer-based systems. In this approach, integrated, multiple-view models capture information relevant to the system under design. The paper considers the Multigraph Architecture framework for model-integrated computing developed at Vanderbilt's Measurement and Computing Systems Laboratory.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Cyber Physical Systems: Design Challenges

Abstract: Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. There are considerable challenges, particularly because the physical components of such systems introduce safety and reliability requirements qualitatively different from those in general- purpose computing. Moreover, physical components are qualitatively different from object-oriented software components. Standard abstractions based on method calls and threads do not work. This paper examines the challenges in designing such systems, and in particular raises the question of whether today's computing and networking technologies provide an adequate foundation for CPS. It concludes that it will not be sufficient to improve design processes, raise the level of abstraction, or verify (formally or otherwise) designs that are built on today's abstractions. To realize the full potential of CPS, we will have to rebuild computing and networking abstractions. These abstractions will have to embrace physical dynamics and computation in a unified way.