Optimizing the Performance of Streaming Numerical Kernels on the IBM Blue Gene/P PowerPC 450 Processor

Several emerging petascale architectures use energy-efficient processors with vectorized computational units and in-order thread processing. On these architectures the sustained performance of streaming numerical kernels, ubiquitous in the solution of partial differential equations, represents a challenge despite the regularity of memory access. Sophisticated optimization techniques are required to fully utilize the Central Processing Unit (CPU). We propose a new method for constructing streaming numerical kernels using a high-level assembly synthesis and optimization framework. We describe an implementation of this method in Python targeting the IBM Blue Gene/P supercomputer's PowerPC 450 core. This paper details the high-level design, construction, simulation, verification, and analysis of these kernels utilizing a subset of the CPU's instruction set. We demonstrate the effectiveness of our approach by implementing several three-dimensional stencil kernels over a variety of cached memory scenarios and analyzing the mechanically scheduled variants, including a 27-point stencil achieving a 1.7x speedup over the best previously published results

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

Data enabling digital ecosystem for sustainable shared electric mobility-as-a-service in smart cities-an innovative business model perspective

Increase in urbanization drives the need for municipalities to make mobility more efficient, both to address climate goals as well as creating a smart living environment for citizens, with less noise congestion, and pollution. As vehicles are being electrified, further advances will be needed to meet social, environmental, and economic sustainability targets, and a more efficient use of vehicles and public transport is central in this endeavor. Accordingly, Electric Mobility as a Service (eMaaS) has developed as a concept with the potential to increase sustainability mobility in cities and been designated as a phenomenon with potential to radically change how people move in the future. But presently there is the lack of a common business model that supports complex integration of all actors, digital technologies, and infrastructures involved in the eMaaS business ecosystem. This study aims to support the further development of eMaaS by providing a state of the art of eMaaS and further proposes a digital ecosystem as a business model for eMaaS sharing in smart cities. Accordingly, a systematic literature review was adopted grounded on secondary data from the literature to offers a new approach to urban mobility and demonstrate the suitability of the eMaaS concept in smart communities. The digital ecosystem is designed based on system design approach. Findings from this study provides a sustainable policy perspective, discusses the challenges and opportunities towards the development of eMaaS and its impact on electrification of vehicles. Overall, findings from this study considers the role of electric vehicles as part of the mobility sharing economy. Recommendations from this study provides designs and strategies for eMaaS, the interrelations between eMobility and other everyday practices, strategically highlighting the positive benefits of eMaaS and broader policies to limit private car usage in cities.publishedVersio

Silicon-based spin and charge quantum computation

Silicon-based quantum-computer architectures have attracted attention because of their promise for scalability and their potential for synergetically utilizing the available resources associated with the existing Si technology infrastructure. Electronic and nuclear spins of shallow donors (e.g. phosphorus) in Si are ideal candidates for qubits in such proposals due to the relatively long spin coherence times. For these spin qubits, donor electron charge manipulation by external gates is a key ingredient for control and read-out of single-qubit operations, while shallow donor exchange gates are frequently invoked to perform two-qubit operations. More recently, charge qubits based on tunnel coupling in P$_2^+$ substitutional molecular ions in Si have also been proposed. We discuss the feasibility of the building blocks involved in shallow donor quantum computation in silicon, taking into account the peculiarities of silicon electronic structure, in particular the six degenerate states at the conduction band edge. We show that quantum interference among these states does not significantly affect operations involving a single donor, but leads to fast oscillations in electron exchange coupling and on tunnel-coupling strength when the donor pair relative position is changed on a lattice-parameter scale. These studies illustrate the considerable potential as well as the tremendous challenges posed by donor spin and charge as candidates for qubits in silicon.Comment: Review paper (invited) - to appear in Annals of the Brazilian Academy of Science