A new AXT format for an efficient SpMV product using AVX-512 instructions and CUDA

The Sparse Matrix-Vector (SpMV) product is a key operation used in many scientific applications. This work proposes a new sparse matrix storage scheme, the AXT format, that improves the SpMV performance on vector capability platforms. AXT can be adapted to different platforms, improving the storage efficiency for matrices with different sparsity patterns. Intel AVX-512 instructions and CUDA are used to optimise the performances of the four different AXT subvariants. Performance comparisons are made with the Compressed Sparse Row (CSR) and AXC formats on an Intel Xeon Gold 6148 processor and an NVIDIA Tesla V100 Graphics Processing Units using 26 matrices. On the Intel platform the overall AXT performance is 18% and 44.3% higher than the AXC and CSR respectively, reaching speed-up factors of up to x7.33. On the NVIDIA platform the AXT performance is 44% and 8% higher than the AXC and CSR performances respectively, reaching speed-up factors of up to x378.5S

Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources

Accessing Big (Commercial) Data across a Global Research Infrastructure - Modelling Consumer Behaviour in China

(1) Business School (2) EPCCThe use of globally distributed computing systems and globally distributed data to understand and manage global organisations is a well-established vision. It can be found in patents awarded for electrical communications systems that are integrated with electro-mechanical computing devices as far back as 1927. Use of electrical communications to reproduce images goes back even further to the first fax patent awarded to Scottish inventor Alexander Bain in 1843, preceding Alexander Graham Bell's patent for the telephone by over 30 years. Like many other company assets, data has value, however it has two additional characteristics that establish tensions with a globally distributed vision: (i) its value cannot be assessed until after it has been analysed, and (ii) that analysis may prove to be of more value to a competitor than the company itself. This type of concern is not typical of the global scientific collaborations that have driven the development of global network infrastructure, a distinction Jim Gray of Microsoft highlighted by describing data exchanged in radio-astronomy collaborations as “completely worthless”, by which he meant that it had all the dimensionality and scale of the most complex problems in business or medicine, but none of the sensitivities that impede how and with whom you share that data, or what analyses you attempt. Since the Economic and Social Research Council defines social science as “the study of society and the manner in which people behave and influence the world around us” it is clear that the sensitivities of exposing commercial data on behaviour in global markets to globally distributed computational environments presents a major challenge for (Social) Data Scientists. This paper describes some of the challenges of building the first Global Computing Grid to connect collaborating sites in three continents and installing an embedded analytical facility within a Chinese commercial organisation that has enabled collaborative analysis of millions of consumers. We report how this access has provided new insights into consumer behaviour within China ranging from testing strategic models of economic development to exploring ‘digital exclusion’ and the impact of migration on technology adoption