Distributed-Memory Breadth-First Search on Massive Graphs

This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.Comment: arXiv admin note: text overlap with arXiv:1104.451

Implementing Push-Pull Efficiently in GraphBLAS

We factor Beamer's push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We show that these graph algorithm optimizations, which together constitute DOBFS, can be neatly and separably described using linear algebra and can be expressed in the GraphBLAS linear-algebra-based framework. We provide experimental evidence that with these optimizations, a DOBFS expressed in a linear-algebra-based graph framework attains competitive performance with state-of-the-art graph frameworks on the GPU and on a multi-threaded CPU, achieving 101 GTEPS on a Scale 22 RMAT graph.Comment: 11 pages, 7 figures, International Conference on Parallel Processing (ICPP) 201

Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs

Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are an ideal platform for graph algorithms due to their high peak performance and memory bandwidth. While there exist several GPU connectivity algorithms in the literature, many design choices have not yet been explored. In this paper, we explore various design choices in GPU connectivity algorithms, including sampling, linking, and tree compression, for both the static as well as the incremental setting. Our various design choices lead to over 300 new GPU implementations of connectivity, many of which outperform state-of-the-art. We present an experimental evaluation, and show that we achieve an average speedup of 2.47x speedup over existing static algorithms. In the incremental setting, we achieve a throughput of up to 48.23 billion edges per second. Compared to state-of-the-art CPU implementations on a 72-core machine, we achieve a speedup of 8.26--14.51x for static connectivity and 1.85--13.36x for incremental connectivity using a Tesla V100 GPU

Internal validation of STRmix™ – A multi laboratory response to PCAST

We report a large compilation of the internal validations of the probabilistic genotyping software STRmix™. Thirty one laboratories contributed data resulting in 2825 mixtures comprising three to six donors and a wide range of multiplex, equipment, mixture proportions and templates. Previously reported trends in the LR were confirmed including less discriminatory LRs occurring both for donors and non-donors at low template (for the donor in question) and at high contributor number. We were unable to isolate an effect of allelic sharing. Any apparent effect appears to be largely confounded with increased contributor number

Chinese students in a UK business school: hearing the student voice in reflective teaching and learning practice.

This paper presents the outcomes of a study carried out in 2001-2002 with nine postgraduate students from China, enrolled on taught master's programmes in a UK university business school. The aims of the research were to explore the development of the students' orientations to learning during their year of study in the UK, and to explore how the researcher's interactions with the study group contributed to her professional reflections and influenced her academic practice. The main conclusions of the project were that participants' underlying approaches to learning did not change substantially over the year, owing to the culturally implicit nature of UK academic conventions and that they experienced high levels of emotional isolation and loneliness, which affected their academic confidence

Antimicrobial proteins and polypeptides in pulmonary innate defence

Inspired air contains a myriad of potential pathogens, pollutants and inflammatory stimuli. In the normal lung, these pathogens are rarely problematic. This is because the epithelial lining fluid in the lung is rich in many innate immunity proteins and peptides that provide a powerful anti-microbial screen. These defensive proteins have anti-bacterial, anti- viral and in some cases, even anti-fungal properties. Their antimicrobial effects are as diverse as inhibition of biofilm formation and prevention of viral replication. The innate immunity proteins and peptides also play key immunomodulatory roles. They are involved in many key processes such as opsonisation facilitating phagocytosis of bacteria and viruses by macrophages and monocytes. They act as important mediators in inflammatory pathways and are capable of binding bacterial endotoxins and CPG motifs. They can also influence expression of adhesion molecules as well as acting as powerful anti-oxidants and anti-proteases. Exciting new antimicrobial and immunomodulatory functions are being elucidated for existing proteins that were previously thought to be of lesser importance. The potential therapeutic applications of these proteins and peptides in combating infection and preventing inflammation are the subject of ongoing research that holds much promise for the future

A Case for Accelerating Software RTL Simulation

RTL simulation is a critical tool for hardware design but its current slow speed often bottlenecks the whole design process. Simulation speed becomes even more crucial for agile and open-source hardware design methodologies, because the designers not only want to iterate on designs quicker, but they may also have less resources with which to simulate them. In this article, we execute multiple simulators and analyze them with hardware performance counters. We find some open-source simulators not only outperform a leading commercial simulator, they also achieve comparable or higher instruction throughput on the host processor. Although advanced optimizations may increase the complexity of the simulator, they do not significantly hinder instruction throughput. Our findings make the case that there is significant room to accelerate software simulation and open-source simulators are a great starting point for researchers