Challenges and opportunities in many-core computing,”

With increasing use of computers that employ many independent processing units, commercial and technical-scientific software, as well as general-purpose operating systems, will have to undergo fundamental changes. By John L. Manferdelli, Naga K. Govindaraju, and Chris Crall ABSTRACT | In this paper, we present some of the challenges and opportunities in software development based on the current hardware trends and the impact of massive parallelism on both the software and hardware industry. We indicate some of the approaches that can enable software development to effectively exploit the many-core architectures. Some of these include encapsulating domain-specific knowledge in reusable components, such as libraries, integrating concurrency with languages, and supporting explicit declarations to help compilers and operating system schedulers. Tighter interaction between software and underlying hardware is required to build scalable and portable applications with predictable performance and higher power-efficiency. Overall, many-core computing provides us opportunities to enable new application scenarios that support enhanced functionality and a richer experience for the user on commodity hardware

Platform Dependent Verification: On Engineering Verification Tools for 21st Century

The paper overviews recent developments in platform-dependent explicit-state LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006

MANOCHA D.: Efficient relational database management using graphics processors

We present algorithms using graphics processing units (GPUs) to efficiently perform database management queries. Our algorithms use efficient data memory representations and storage models on GPUs to perform fast database computations. We present relational database algorithms that successfully exploit the high memory bandwidth and the inherent parallelism available in GPUs. We implement these algorithms on commodity GPUs and compare their performance with optimized CPU-based algorithms. We show that the GPUs can be used as a co-processor to accelerate many database and data mining queries. 1

ABSTRACT Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors

We present algorithms for fast quantile and frequency estimation in large data streams using graphics processor units (GPUs). We exploit the high computational power and memory bandwidth of graphics processors and present a novel sorting algorithm that performs rasterization operations on the GPUs. We use sorting as the main computational component for histogram approximation and the construction of ɛ-approximate quantile and frequency summaries. Our overall algorithms for numerical statistics computation on data streams are deterministic, applicable to fixed or variablesized sliding windows and use a limited memory footprint. We use the GPU as a co-processor and minimize the data transmission between the CPU and GPU by taking into account the low bus bandwidth. We have implemented our algorithms on a PC with a NVIDIA GeForce FX 6800 Ultra GPU and a 3.4 GHz Pentium IV CPU and applied them to large data streams consisting of more than 100 million values. We have compared their performance against optimized implementations of prior CPU-based algorithms. Overall, our results demonstrate that the graphics processor available on a commodity computer system is an efficient stream-processor and a useful co-processor for mining data streams