Acceleration of Distance-to-Default with GPU
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Distance-to-Default(DTD), which is used to describe the default risk of a rm, acts an important role in credit research. Nowadays, since we can access a large amount of historical data, we can get a more accurate DTD result. However, this directly increases the computation time as well as the computation power. Meanwhile, Graphic Processing Unit(GPU), with its tremendous capability of parallel computing, becomes more and more popular in High Performance Computing. In addition, CUDA, a parallel computing platform and programming model that invented by Nvidia, makes GPU programming much more easier and faster. So GPU can be a proper candidate to accelerate the DTD processing if we can ooad the most computation intensive part to the GPU.
In this thesis project, the author explore the existing DTD program(provided by RMI, NUS), analyze the algorithm and ooad the most computation intensive part to GPU using CUDA. The platform used for testing consists of an 2.4GHz Intel i5 CPU and a Nvidia GeForce GTX460 GPU. The GPU is used as a co-processor, which is connected to the mother board with PCIe2 bus.
Place, publisher, year, edition, pages
2012. , 34 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-102877OAI: oai:DiVA.org:kth-102877DiVA: diva2:557179
Master of Science - System-on-Chip Design
Zheng, Lirong, Professor