PLC 2012 - 2012 International Workshop on Multicore and GPU Programming Models, Languages and Compilers

Due to power and cooling constraints, recent performance improvements in both general-purpose and special-purpose processors have come primarily from increased on-chip parallelism rather than increased clock rates. On-chip parallelism with Multicore processors and GPU accelerators are now ubiquitous and have received significant attention in the past of a few years. Parallelism is therefore of considerable interest to a much broader group than developers of parallel applications for high-end supercomputers. This shift to an increasing on-chip parallelism will place new burdens on application software developers who are facing a daunting task of parallelizing general non-numerical applications and must have reasonable knowledge of multicore processor and GPU accelerator architectures, programming models, languages, compilers and new software tools for GPU and Multicore platforms. Several programming environments have recently emerged in response to the need to develop applications for GPUs and multi-core processors. This workshop provides a forum for the presentation of research on all aspects of GPU and multicore processors programming models, compiler optimizations, language extensions, and software tools for GPU and Multicore processor platforms.
Areas of interest include but are not limited to the following topics:
Multicore processors and GPU accelerators
Multicore and GPU Programming models: thread and task based models, data parallel models, stream programming
Language extensions for GPU programming/environments:
C/C++ extensions for GPU programming
OpenMP extensions for Accelerator
OpenCL
CUDA
CAL
CAPS/HMPP
Compiler optimizations and tuning for GPU accelerator and Multicore processors
SIMDization/Vectorization
Parallelization and locality optimizations
Reducing synchronization and scheduling overheads on GPU and Multicore
Tiling, parametric tiling and offloading
Runtime systems for Multicore processor and GPU accelerators
Debuggers, and performance analysis tools for multicore processor and GPU platforms
Operating systems and virtual shared memory for CPU and GPU heterogeneous chip
Software tools for discovering parallelism
Application frameworks, design patterns, and domain-specific languages for developing manycore applications