Machine learning for fiber nonlinearity mitigation in long-haul coherent optical transmission systems

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation

Preemptive Edge Application Instantiation

Cloud-edge computing is a paradigm that brings servers physically closer to end-users, e.g., by co-locating servers and cellular towers. Cloud-edge computing can reduce latency and improve performance for mobile applications. For cloud-edge computing to deliver a superior user experience, the location of application instantiation is optimally selected to be close to the user. However, as a user moves, an initially optimal location becomes unsuitable. This disclosure describes techniques to predict the next possible edge zones for a cloud-edge application to maintain performance and service continuity as the mobile device moves. The techniques leverage factors such as mobility history, network characteristics, performance data, etc. to preemptively instantiate the application on predicted edge zones to ensure service continuity at a high performance and with low latency

BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments

Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process

Two research contributions in 64-bit computing: Testing and Applications

Following the release of Windows 64-bit and Redhat Linux 64-bit operating systems (OS) in late April 2005, this is the one of the first 64-bit OS research project completed in a British university. The objective is to investigate (1) the increase/decrease in performance compared to 32-bit computing; (2) the techniques used to develop 64-bit applications; and (3) how 64-bit computing should be used in IT and research organizations to improve their work. This paper summarizes research discoveries for this investigation, including two major research contributions in (1) testing and (2) application development. The first contribution includes performance, stress, application, multiplatform, JDK and compatibility testing for AMD and Intel models. Comprehensive testing results reveal that 64-bit computing has a better performance in application performance, system performance and stress testing, but a worse performance in compatibility testing than the traditional 32-bit computing. A 64-bit dual-core processor has been tested and the results show that it performs better than a 64-bit single-core processor, but only in application that requires very high demands of CPU and memory consumption. The second contribution is .NET 1.1 64-bit implementations. Without additional troubleshooting, .NET 1.1 does not work on 64-bit Windows operating systems in stable ways. After stabilizing .NET environment, the next step is the application development, which is a dynamic repository with functions such as registration, download, login-logout, product submissions, database storage and statistical reports. The technology is based on Visual Studio .NET 2003, .NET 1.1 Framework with Service Pack 1, SQL Server 2000 with Service Pack 4 and IIS Server 6.0 on the Windows Server 2003 Enterprise x64 platform with Service Pack 1

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie