The VINEYARD Approach: Versatile, Integrated, Accelerator-Based, Heterogeneous Data Centres.

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters. Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator. The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application. We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application. Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored. The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.

The Financial Accountability of Irish Credit Unions: an initial empirical study

Credit unions are member-owned, voluntary, self-help, democratic, not-for-profit institutions that provide financial services to their members. They have both economic and social goals. Over this last decade they have achieved remarkable growth levels and currently there are 600 such organisations in Ireland, with approximately 50 per cent of the adult population of Ireland belonging to a credit union. Accounting for credit unions is a much-neglected area and relatively little is known about the sector's accountability. This paper presents the results of an initial empirical study of the financial accountability of Irish credit unions. A series of interviews and a basic content analysis of 178 recent financial statements were used to identify the views of key stakeholders with respect to the discharge of financial accountability by credit unions and the current quality of financial reporting. Overall, the research points to a sector where financial accountability through the medium of the annual report is weak and possible adverse consequences of this are explored. On the basis of the interviews it is suggested that if changes in financial accountability are to be achieved then some more proactive engagement of parties external to the management of individual credit unions is needed.