19,121 research outputs found
Abstract State Machines 1988-1998: Commented ASM Bibliography
An annotated bibliography of papers which deal with or use Abstract State
Machines (ASMs), as of January 1998.Comment: Also maintained as a BibTeX file at http://www.eecs.umich.edu/gasm
A Similarity Measure for GPU Kernel Subgraph Matching
Accelerator architectures specialize in executing SIMD (single instruction,
multiple data) in lockstep. Because the majority of CUDA applications are
parallelized loops, control flow information can provide an in-depth
characterization of a kernel. CUDAflow is a tool that statically separates CUDA
binaries into basic block regions and dynamically measures instruction and
basic block frequencies. CUDAflow captures this information in a control flow
graph (CFG) and performs subgraph matching across various kernel's CFGs to gain
insights to an application's resource requirements, based on the shape and
traversal of the graph, instruction operations executed and registers
allocated, among other information. The utility of CUDAflow is demonstrated
with SHOC and Rodinia application case studies on a variety of GPU
architectures, revealing novel thread divergence characteristics that
facilitates end users, autotuners and compilers in generating high performing
code
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Internet of Things(IoT) devices, mobile phones, and robotic systems are often
denied the power of deep learning algorithms due to their limited computing
power. However, to provide time-critical services such as emergency response,
home assistance, surveillance, etc, these devices often need real-time analysis
of their camera data. This paper strives to offer a viable approach to
integrate high-performance deep learning-based computer vision algorithms with
low-resource and low-power devices by leveraging the computing power of the
cloud. By offloading the computation work to the cloud, no dedicated hardware
is needed to enable deep neural networks on existing low computing power
devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the
power of using cloud computing to perform real-time vision tasks. Furthermore,
to reduce latency and improve real-time performance, compression algorithms are
proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV
2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser
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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
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