ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology [ISMB] 2016, Orlando, Florida)

Ebola outbreak highlights the need for wet and dry laboratory collaboration

The recent Ebola outbreak in Western Africa taught us that Ebolaviruses can cause much larger outbreaks and represent a much greater health threat than many of us believed (or wanted to believe). As of 30th March, the outbreak had resulted in 28,646 confirmed cases and 11,323 deaths. Although the WHO stated that the Ebola epidemic in West Africa no longer represents a Public Health Emergency of International Concern, since Guinea, Liberia, and Sierra are now capable of controlling and maintaining further small outbreaks, flare-ups still occur, most recently, on 4th April when two new cases were reported in Liberia (www.who.int)

Scheduling and Tuning Kernels for High-performance on Heterogeneous Processor Systems

Accelerated parallel computing techniques using devices such as GPUs and Xeon Phis (along with CPUs) have proposed promising solutions of extending the cutting edge of high-performance computer systems. A significant performance improvement can be achieved when suitable workloads are handled by the accelerator. Traditional CPUs can handle those workloads not well suited for accelerators. Combination of multiple types of processors in a single computer system is referred to as a heterogeneous system. This dissertation addresses tuning and scheduling issues in heterogeneous systems. The first section presents work on tuning scientific workloads on three different types of processors: multi-core CPU, Xeon Phi massively parallel processor, and NVIDIA GPU; common tuning methods and platform-specific tuning techniques are presented. Then, analysis is done to demonstrate the performance characteristics of the heterogeneous system on different input data. This section of the dissertation is part of the GeauxDock project, which prototyped a few state-of-art bioinformatics algorithms, and delivered a fast molecular docking program. The second section of this work studies the performance model of the GeauxDock computing kernel. Specifically, the work presents an extraction of features from the input data set and the target systems, and then uses various regression models to calculate the perspective computation time. This helps understand why a certain processor is faster for certain sets of tasks. It also provides the essential information for scheduling on heterogeneous systems. In addition, this dissertation investigates a high-level task scheduling framework for heterogeneous processor systems in which, the pros and cons of using different heterogeneous processors can complement each other. Thus a higher performance can be achieve on heterogeneous computing systems. A new scheduling algorithm with four innovations is presented: Ranked Opportunistic Balancing (ROB), Multi-subject Ranking (MR), Multi-subject Relative Ranking (MRR), and Automatic Small Tasks Rearranging (ASTR). The new algorithm consistently outperforms previously proposed algorithms with better scheduling results, lower computational complexity, and more consistent results over a range of performance prediction errors. Finally, this work extends the heterogeneous task scheduling algorithm to handle power capping feature. It demonstrates that a power-aware scheduler significantly improves the power efficiencies and saves the energy consumption. This suggests that, in addition to performance benefits, heterogeneous systems may have certain advantages on overall power efficiency