82,160 research outputs found
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Compressive sensing promises to enable bandwidth-efficient on-board
compression of astronomical data by lifting the encoding complexity from the
source to the receiver. The signal is recovered off-line, exploiting GPUs
parallel computation capabilities to speedup the reconstruction process.
However, inherent GPU hardware constraints limit the size of the recoverable
signal and the speedup practically achievable. In this work, we design parallel
algorithms that exploit the properties of circulant matrices for efficient
GPU-accelerated sparse signals recovery. Our approach reduces the memory
requirements, allowing us to recover very large signals with limited memory. In
addition, it achieves a tenfold signal recovery speedup thanks to ad-hoc
parallelization of matrix-vector multiplications and matrix inversions.
Finally, we practically demonstrate our algorithms in a typical application of
circulant matrices: deblurring a sparse astronomical image in the compressed
domain
Parallel sparse matrix-vector multiplication as a test case for hybrid MPI+OpenMP programming
We evaluate optimized parallel sparse matrix-vector operations for two
representative application areas on widespread multicore-based cluster
configurations. First the single-socket baseline performance is analyzed and
modeled with respect to basic architectural properties of standard multicore
chips. Going beyond the single node, parallel sparse matrix-vector operations
often suffer from an unfavorable communication to computation ratio. Starting
from the observation that nonblocking MPI is not able to hide communication
cost using standard MPI implementations, we demonstrate that explicit overlap
of communication and computation can be achieved by using a dedicated
communication thread, which may run on a virtual core. We compare our approach
to pure MPI and the widely used "vector-like" hybrid programming strategy.Comment: 12 pages, 6 figure
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