Run-time optimization of adaptive irregular applications

Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Message Passing with Communication Structures

Institute for Computing Systems ArchitectureAbstraction concepts based on process groups have largely dominated the design and implementation of communication patterns in message passing systems. Although such an approach seems pragmatic—given that participating processes form a ‘group’—in this dissertation, we discuss subtle issues that affect the qualitative and quantitative aspects of this approach. To address these issues, we introduce the concept of a ‘communication structure,’ which defines a communication pattern as an implicit runtime composition of localised patterns, known as ‘roles.’ During application development, communication structures are derived from the algorithm being implemented. These are then translated to an executable form by defining process specific data structures, known as ‘branching channels.’ The qualitative advantages of the communication structure approach are that the resulting programming model is non-ambiguous, uniform, expressive, and extensible. To use a pattern is to access the corresponding branching channels; to define a new pattern is simply to combine appropriate roles. The communication structure approach therefore allows immediate implementation of ad hoc patterns. Furthermore, it is guaranteed that every newly added role interfaces correctly with all of the existing roles, therefore scaling the benefit of every new addition. Quantitatively, branching channels improve performance by automatically overlapping computations and communications. The runtime system uses a receiver initiated communication protocol that allows senders to continue immediately without waiting for the receivers to respond. The advantage is that, unlike split-phase asynchronous communications, senders need not check whether the send operations were successful. Another property of branching channels is that they allow communications to be grouped, identified, and referenced. Communication structure specific parameters, such as message buffering, can therefore be specified immediately. Furthermore, a ‘commit’ based interface optimisation for send-and-forget type communications—where senders do not reuse sent data—is presented. This uses the referencing property of branching channels, allowing message buffering without incurring performance degradation due to intermediate memory copy

Run-time optimization of adaptive irregular applications

Compared to traditional compile-time optimization, run-time optimization could offer significant performance improvements when parallelizing and optimizing adaptive irregular applications, because it performs program analysis and adaptive optimizations during program execution. Run-time techniques can succeed where static techniques fail because they exploit the characteristics of input data, programs' dynamic behaviors, and the underneath execution environment. When optimizing adaptive irregular applications for parallel execution, a common observation is that the effectiveness of the optimizing transformations depends on programs' input data and their dynamic phases. This dissertation presents a set of run-time optimization techniques that match the characteristics of programs' dynamic memory access patterns and the appropriate optimization (parallelization) transformations. First, we present a general adaptive algorithm selection framework to automatically and adaptively select at run-time the best performing, functionally equivalent algorithm for each of its execution instances. The selection process is based on off-line automatically generated prediction models and characteristics (collected and analyzed dynamically) of the algorithm's input data, In this dissertation, we specialize this framework for automatic selection of reduction algorithms. In this research, we have identified a small set of machine independent high-level characterization parameters and then we deployed an off-line, systematic experiment process to generate prediction models. These models, in turn, match the parameters to the best optimization transformations for a given machine. The technique has been evaluated thoroughly in terms of applications, platforms, and programs' dynamic behaviors. Specifically, for the reduction algorithm selection, the selected performance is within 2% of optimal performance and on average is 60% better than "Replicated Buffer," the default parallel reduction algorithm specified by OpenMP standard. To reduce the overhead of speculative run-time parallelization, we have developed an adaptive run-time parallelization technique that dynamically chooses effcient shadow structures to record a program's dynamic memory access patterns for parallelization. This technique complements the original speculative run-time parallelization technique, the LRPD test, in parallelizing loops with sparse memory accesses. The techniques presented in this dissertation have been implemented in an optimizing research compiler and can be viewed as effective building blocks for comprehensive run-time optimization systems, e.g., feedback-directed optimization systems and dynamic compilation systems

Practical implementation and performance analysis on security of sensor networks

A wireless sensor network (WSN) is a network made of thousands of sensing elements called as nodes with wireless capabilities. Their application is varied and diverse ranging from military to domestic and household. As the world of self-organizing sensor networks tip to the edge of maximum utilization, their wider deployment is adding pressure on the security front. Powerful laptops and workstations make it more challenging for small sensors. In addition, there are many security challenges in WSN, e.g- confidentiality, authentication, freshness, integrity etc. Contributions of this work are as follows: “Symmetric” security implementation: This thesis work designs a symmetric-key based security in sensor hardware in the Link layer of sensor network protocols. Link Layer security can protect a wireless network by denying access to the network itself before a user is successfully authenticated. This prevents attacks against the network infrastructure and protects the network from devastating attacks. “Public key” implementation in sensor hardware: Asymmetric key techniques are attractive for authentication data or session keys. Traditional schemes like RSA require considerable amounts of resources which in the past has limited their use. This thesis has implemented Elliptic Curve Cryptography (ECC) in Mica2 hardware, which is an approach to public-key cryptography based on the mathematics of elliptic curves. Quantitative overhead analysis: This thesis work analyzes the wireless communication overhead (No. of packets transmitted) vs the (transmit and receive) energy consumed in mJoules and memory storage overhead (bytes) for ECC as compared to the symmetric counterpart for the implemented WSN security protocols

Advanced Information Systems and Technologies

This book comprises the proceedings of the VI International Scientific Conference “Advanced Information Systems and Technologies, AIST-2018”. The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing, computer networking and telecomunications, modern methods and information technologies of sustainable development. They will be useful for students, graduate students, researchers who interested in computer science