high performance computing on graphics processing units: hgpu.org

Section 1: Algorithms

Design, analysis, and implementation of parallel algorithms in science and engineering, focusing on issues such as

Scalability and speedup

Efficient utilization of the memory hierarchy

Communication and synchronization

Data Management and Exploration

Energy Efficiency.

The parallel computing aspects should be emphasized.

Section 2: Software and Architectures

Software engineering for developing and maintaining parallel software, including

Parallel programming languages, compilers, and environments

Tools and techniques for generating reliable and efficient parallel code

Testing and debugging techniques and tools

Best practices of parallel computing on multicore, manycore, and stream processors

Software, architectures and operating systems for all types of parallel computers may be considered, including multicores, GPU accelerators, FPGA reconfigurable systems, high-end machines and cloud computing.

Section 3: Applications

The application of parallel computing to solve all types of business, industrial, scientific, and engineering problems using high-performance computing technologies, in particular:

Applications of high-end computers, including exascale

Applications of multicore / manycore processors

Applications for heterogeneous systems, including FPGAs, GPUs, etc.

Cloud and Grid computing applications

Data intensive (Big Data) analytics and applications.