Parallelism increases iterative learning power

AbstractIterative learning (It-learning) is a Gold-style learning model in which each of a learner’s output conjectures may depend only upon the learner’s current conjecture and the current input element. Two extensions of the It-learning model are considered, each of which involves parallelism. The first is to run, in parallel, distinct instantiations of a single learner on each input element. The second is to run, in parallel, n individual learners incorporating the first extension, and to allow the n learners to communicate their results. In most contexts, parallelism is only a means of improving efficiency. However, as shown herein, learners incorporating the first extension are more powerful than It-learners, and, collective learners resulting from the second extension increase in learning power as n increases. Attention is paid to how one would actually implement a learner incorporating each extension. Parallelism is the underlying mechanism employed

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