Abell, Stephen W. MSECE, Purdue University, August 2013. Parallel Acceleration of Deadlock Detection and Avoidance Algorithms on GPUs. Major Professor: Dr. John Jaehwan Lee. Current mainstream computing systems have become increasingly complex. Most of which have Central Processing Units (CPUs) that invoke multiple threads for their computing tasks. The growing issue with these systems is resou...

Recent advances in GPUs (graphics processing units) lead to massively parallel hardware that is easily programmable and widely applied in areas which require intensive computation besides graphics acceleration. The appearance of GPU clusters gains popularity in the scientific computing community, and the study on GPU clusters becomes an increasingly hot issue. While extending a singleGPU system...

As data sets grow and conventional processor performance scaling slows, data analytics move towards heterogeneous architectures that incorporate hardware accelerators (notably GPUs) to continue scaling performance. However, existing GPU-based databases fail to deal with big data applications efficiently: their execution model suffers from scalability limitations on GPUs whose memory capacity is...

Multi-core accelerators offer significant potential to improve the performance of parallel applications. However, tools to help the parallel application developer understand accelerator performance and its impact are scarce. An approach is presented to measure the performance of GPU computations programmed using CUDA and integrate this information with application performance data captured with...

The aim of the paper is to show how to design and implement fast parallel algorithms for Linear Congruential, Lagged Fibonacci and Wichmann-Hill pseudorandom number generators. The new algorithms employ the divide-and-conquer approach for solving linear recurrence systems. They are implemented on multi GPU-accelerated systems using CUDA. Numerical experiments performed on a computer system with...