Commodity computing

Abstract: Developed more than 30 years ago to address mainframe computing problems, virtual machine monitors have resurfaced on commodity platforms, offering novel solutions to challenges in security, reliability, and administration Stanford University researchers began to look at the potential of virtual machines to overcome difficulties that hardware and operating system limitations imposed: This time the problems stemmed from massively parallel processing (MPP) machines that were difficult to program and could not run existing operating systems With virtual machines, researchers found they could make these unwieldy architectures look sufficiently similar to existing platforms to leverage the current operating systems From this project came the people and ideas that underpinned VMware Inc, the original supplier of VMMs for commodity computing hardware The implications of having a VMM for commodity platforms intrigued both researchers and entrepreneurs

Abstract: Existing processes for patients' vital data collection require a great deal of labor work to collect, input and analyze the information. These processes are usually slow and error-prone, introducing a latency that prevents real-time data accessibility. This scenario restrains the clinical diagnostics and monitoring capabilities. We propose a solution to automate this process by using “sensors” attached to existing medical equipments that are inter-connected to exchange service. The proposal is based on the concepts of utility computing and wireless sensor networks. The information becomes available in the “cloud” from where it can be processed by expert systems and/or distributed to medical staff. The proof-of-concept design applies commodity computing integrated to legacy medical devices, ensuring cost-effectiveness and simple integration.

Abstract: We describe the development of an FX-style correlator for very long baseline interferometry (VLBI), implemented in software and intended to run in multiprocessor computing environments, such as large clusters of commodity machines (Beowulf clusters) or computers specifically designed for high-performance computing, such as multiprocessor shared-memory machines. We outline the scientific and practical benefits for VLBI correlation, these chiefly being due to the inherent flexibility of software and the fact that the highly parallel and scalable nature of the correlation task is well suited to a multiprocessor computing environment. We suggest scientific applications where such an approach to VLBI correlation is most suited and will give the best returns. We report detailed results from the Distributed FX (DiFX) software correlator running on the Swinburne supercomputer (a Beowulf cluster of ∼300 commodity processors), including measures of the performance of the system. For example, to correlate all Stokes products for a 10 antenna array with an aggregate bandwidth of 64 MHz per station, and using typical time and frequency resolution, currently requires an order of 100 desktop- class compute nodes. Due to the effect of Moore's law on commodity computing performance, the total number and cost of compute nodes required to meet a given correlation task continues to decrease rapidly with time. We show detailed comparisons between DiFX and two existing hardware-based correlators: the Australian Long Baseline Array S2 correlator and the NRAO Very Long Baseline Array correlator. In both cases, excellent agreement was found between the correlators. Finally, we describe plans for the future operation of DiFX on the Swinburne supercomputer for both astrophysical and geodetic science.

Abstract: We describe the development of an FX style correlator for Very Long Baseline Interferometry (VLBI), implemented in software and intended to run in multi-processor computing environments, such as large clusters of commodity machines (Beowulf clusters) or computers specifically designed for high performance computing, such as multi-processor shared-memory machines. We outline the scientific and practical benefits for VLBI correlation, these chiefly being due to the inherent flexibility of software and the fact that the highly parallel and scalable nature of the correlation task is well suited to a multi-processor computing environment. We suggest scientific applications where such an approach to VLBI correlation is most suited and will give the best returns. We report detailed results from the Distributed FX (DiFX) software correlator, running on the Swinburne supercomputer (a Beowulf cluster of approximately 300 commodity processors), including measures of the performance of the system. For example, to correlate all Stokes products for a 10 antenna array, with an aggregate bandwidth of 64 MHz per station and using typical time and frequency resolution presently requires of order 100 desktop-class compute nodes. Due to the effect of Moore's Law on commodity computing performance, the total number and cost of compute nodes required to meet a given correlation task continues to decrease rapidly with time. We show detailed comparisons between DiFX and two existing hardware-based correlators: the Australian Long Baseline Array (LBA) S2 correlator, and the NRAO Very Long Baseline Array (VLBA) correlator. In both cases, excellent agreement was found between the correlators. Finally, we describe plans for the future operation of DiFX on the Swinburne supercomputer, for both astrophysical and geodetic science.