Accelerated Event-by-Event Neutrino Oscillation Reweighting with Matter Effects on a GPU

Oscillation probability calculations are becoming increasingly CPU intensive in modern neutrino oscillation analyses. The independency of reweighting individual events in a Monte Carlo sample lends itself to parallel implementation on a Graphics Processing Unit. The library "Prob3++" was ported to the GPU using the CUDA C API, allowing for large scale parallelized calculations of neutrino oscillation probabilities through matter of constant density, decreasing the execution time by a factor of 75, when compared to performance on a single CPU.Comment: Final Update: Post submission update Updated version: quantified the difference in event rates for binned and event-by-event reweighting with a typical binning scheme. Improved formatting of reference

Distributed Block Coordinate Descent for Minimizing Partially Separable Functions

In this work we propose a distributed randomized block coordinate descent method for minimizing a convex function with a huge number of variables/coordinates. We analyze its complexity under the assumption that the smooth part of the objective function is partially block separable, and show that the degree of separability directly influences the complexity. This extends the results in [Richtarik, Takac: Parallel coordinate descent methods for big data optimization] to a distributed environment. We first show that partially block separable functions admit an expected separable overapproximation (ESO) with respect to a distributed sampling, compute the ESO parameters, and then specialize complexity results from recent literature that hold under the generic ESO assumption. We describe several approaches to distribution and synchronization of the computation across a cluster of multi-core computers and provide promising computational results.Comment: in Recent Developments in Numerical Analysis and Optimization, 201

Dissecting sequential programs for parallelization-An approach based on computational units

When trying to parallelize a sequential program, programmers routinely struggle during the first step: finding out which code sections can be made to run in parallel. While identifying such code sections, most of the current parallelism discovery techniques focus on specific language constructs. In contrast, we propose to concentrate on the computations performed by a program. In our approach, a program is treated as a collection of computations communicating with one another using a number of variables. Each computation is represented as a computational unit (CU). A CU contains the inputs and outputs of a computation, and the three phases of a computation are read, compute, and write. Based on the notion of CU, which ensures that the read phase executes before the write phase, we present a unified framework to identify both loop parallelism and task parallelism in sequential programs. We conducted a range of experiments on 23 applications from four different benchmark suites. Our approach accurately identified the parallelization opportunities in benchmark applications based on comparison with their parallel versions. We have also parallelized the opportunities identified by our approach that were not implemented in the parallel versions of the benchmarks and reported the speedup