Summary Parallelization strategies are presented for Virtual Quake, a numerical simulation code for earthquakes based on topologically realistic systems of interacting earthquake faults. One of the demands placed upon the simulation is the accurate reproduction of the observed earthquake statistics over three to four decades. This requires the use of a high‐resolution fault model in computations, which demands computational power that is well beyond the scope of off‐the‐shelf multi‐core CPU computers. However, the recent advances in general‐purpose graphic processing units have the potential to address this problem at moderate cost increments. A functional decomposition of Virtual Quake is performed, and opportunities for parallelization are discussed in this work. Computationally intensive modules are identified, and these are implemented on graphics processing units, significantly speeding up earthquake simulations. In the current best case scenario, a computer with six graphics processing units can simulate 500 years of fault activity in California at 1.5 km × 1.5 km element resolution in less than 1 hour, whereas a single CPU requires more than 2 days to perform the same simulation. Copyright © 2015 John Wiley & Sons, Ltd.

Understanding and modeling resources of Internet end hosts are essential for the design of desktop software and Internet-distributed applications. In this paper, we develop a correlated resource model of Internet end hosts based on real-trace data taken from several volunteer computing projects, including SETI@home. This data cover a five-year period with statistics for 6.7 million hosts. Our resource model is based on statistical analysis of host computational power, memory, and storage as well as how these resources change over time and the correlations among them. We find that resources with few discrete values (core count, memory) are well modeled by approximations governing the change of relative resource quantities over time. Resources with a continuous range of values are well modeled by correlated log-normal distributions (cache, processor speed, and available disk space). We validate and show the utility of the model by applying it to a resource allocation problem for Internet-distributed applications, and compare it to other models. We also make our trace data and tool for automatically generating realistic Internet end hosts publicly available.

Understanding and modelling resources of Internet end hosts is essential for the design of desktop software and Internet-distributed applications. In this paper we develop a correlated resource model of Internet end hosts based on real trace data taken from the SETI@home project. This data covers a 5-year period with statistics for 2.7 million hosts. The resource model is based on statistical analysis of host computational power, memory, and storage as well as how these resources change over time and the correlations between them. We find that resources with few discrete values (core count, memory) are well modeled by exponential laws governing the change of relative resource quantities over time. Resources with a continuous range of values are well modeled with either correlated normal distributions (processor speed for integer operations and floating point operations) or log-normal distributions (available disk space). We validate and show the utility of the models by applying them to a resource allocation problem for Internet-distributed applications, and demonstrate their value over other models. We also make our trace data and tool for automatically generating realistic Internet end hosts publicly available.

As supercomputers and clusters increase in size and complexity, system failures are inevitable. Different hardware components (such as memory, disk, or network) of such systems can have different failure rates. Prior works assume failures equally affect an application, whereas our goal is to provide failure models for applications that reflect their specific component usage. This is challenging because component failure dynamics are heterogeneous in space and time.

This paper describes the implementation of a large bandwidth multi-GPU signal processing system for radio astronomy observation. This system performs very large Fast Fourier Transform (FFT) and spectrum analysis to achieve real-time analysis of a large bandwidth spectrum. This is accomplished by implementing a four-step FFT algorithm in Compute Unified Device Architecture (CUDA). The key feature of this implementation is that the data size transferred between CPU and GPU is reduced using redundant calculation. We also apply pipeline execution to our system to minimize idle processor time, even with multiple GPUs on a shared bus. Using a single GPU, this system can analyze 1 GB of signal data (128 MHz bandwidth at 1 Hz resolution in single precision floating-point complex format) in 0.44 seconds. With the multi-GPU setup, using four GPUs enables 4 GB of signal data to be processed in 0.82 seconds. This is equivalent to a processing speed of around 60 GFLOPS. In particular, we focus on using this system in the Search for Extraterrestrial Radio Emissions from Nearby Developed Intelligent Populations (SERENDIP) project. By using multiple GPUs we can get enough practical performance for high bandwidth radio astronomy projects such as SERENDIP.

We describe a general purpose master-worker parallel computation Python module called PyMW. PyMW is intended to support rapid development, testing and deployment of large scale master-worker style computations on a desktop grid or volunteer computing environment. This module targets non-expert computer users by hiding complicated task submission and result retrieval procedures behind a simple interface. PyMW also provides a unified interface to multiple computing environments with easy extension to support additional environments. In this paper, we describe the internal structure and external interface to the PyMW module and its support for the Condor computing environment and the Berkeley Open Infrastructure for Network Computing (BOINC) platform. We demonstrate the effectiveness and scalability of PyMW by performing master-worker style computations on a desktop grid using Condor and a BOINC volunteer computing project.

Internet based volunteer computing projects such as SETI@home are currently restricted to performing coarse grained, embarrassingly parallel master-worker style tasks. This is partly due to the “pull” nature of task distribution in volunteer computing environments, where workers request tasks from the master rather than the master assigning tasks to arbitrary workers. In this paper we propose algorithms for computing batches of medium grained tasks with deadlines in pull-style volunteer computing environments. We develop models of unreliable workers based on analysis of trace data from an actual volunteer computing project. These models are used to develop algorithms for task distribution in volunteer computing systems with a high probability of meeting batch deadlines. We develop algorithms for perfectly reliable workers, computation-reliable workers and unreliable workers. Finally, we demonstrate the effectiveness of the algorithms through simulations using traces from actual volunteer computing environments.

Internet based volunteer computing projects such as SETI@home are currently restricted to performing coarse grained, embarrassingly parallel tasks. This is partly due to the "pull" nature of task distribution in volunteer computing environments, where workers request tasks from the master rather than the master assigning tasks to arbitrary workers. In this paper we develop algorithms for computing batches of medium grained tasks with soft deadlines in pull- style volunteer computing environments. Using assumptions about worker availability intervals based on previous studies, we develop models of unreliable workers in volunteer computing environments. These models are used to develop algorithms for task distribution in volunteer computing systems with a high probability of meeting batch deadlines. We develop algorithms for perfectly reliable workers, computation-reliable workers and unreliable workers. The effectiveness of the algorithms is demonstrated by using traces from actual execution environments.

In this paper, we examine load distributions to minimize total run time in multi-cluster parallel computing algorithms by applying divisible load theory techniques. Even with homogeneous processor speeds, parallel computations in multi-clusters that evenly assign load can run at less than maximum efficiency due to communication heterogeneity. Using a modified version of the LogP parallel computing model, we propose a general technique of assigning load among multiple clusters to minimize the time each processor spends waiting. This technique is used to determine optimal load distribution for spin glass simulation and parallel bucket sort in multi-cluster systems. It also allows fast analysis of the effects of adding processors or clusters to the computation. We experimentally demonstrate the accuracy of our model, and show how it eliminates wait time in multi-cluster parallel computations. Using load distributions derived from our technique results in an execution time decrease of up to 50%, depending on the degree of heterogeneity among clusters and communication characteristics of the computation.

Although BOA is effective at finding solutions for optimization problems, small population sizes in a model can result in premature convergence to a sub-optimal solution. One way of avoiding premature convergence is to increase population diversity with a mutation operator. In our experiments, we compare several mutation operators for use with BOA. We examine in detail the probabilistic model utilizing (PMU) bit flipping mutation operator. We compare the effectiveness of the PMU operator with standard BOA, self-adaptive evolution and local search of substructural neighborhoods.