3 research outputs found
Runtime Adaptive Hybrid Query Engine based on FPGAs
This paper presents the fully integrated hardware-accelerated query engine for large-scale datasets in the context of Semantic Web databases. As queries are typically unknown at design time, a static approach is not feasible and not flexible to cover a wide range of queries at system runtime. Therefore, we introduce a runtime reconfigurable accelerator based on a Field Programmable Gate Array (FPGA), which transparently incorporates with the freely available Semantic Web database LUPOSDATE. At system runtime, the proposed approach dynamically generates an optimized hardware accelerator in terms of an FPGA configuration for each individual query and transparently retrieves the query result to be displayed to the user. During hardware-accelerated execution the host supplies triple data to the FPGA and retrieves the results from the FPGA via PCIe interface. The benefits and limitations are evaluated on large-scale synthetic datasets with up to 260 million triples as well as the widely known Billion Triples Challenge
Topics in Power Usage in Network Services
The rapid advance of computing technology has created a world powered
by millions of computers. Often these computers are idly consuming energy
unnecessarily in spite of all the efforts of hardware manufacturers. This thesis
examines proposals to determine when to power down computers without
negatively impacting on the service they are used to deliver, compares and
contrasts the efficiency of virtualisation with containerisation, and investigates
the energy efficiency of the popular cryptocurrency Bitcoin.
We begin by examining the current corpus of literature and defining the key
terms we need to proceed.
Then we propose a technique for improving the energy consumption of servers
by moving them into a sleep state and employing a low powered device to act
as a proxy in its place.
After this we move on to investigate the energy efficiency of virtualisation and
compare the energy efficiency of two of the most common means used to do
this.
Moving on from this we look at the cryptocurrency Bitcoin. We consider the
energy consumption of bitcoin mining and if this compared with the value of
bitcoin makes this profitable.
Finally we conclude by summarising the results and findings of this thesis.
This work increases our understanding of some of the challenges of energy
efficient computation as well as proposing novel mechanisms to save energy