Exposing errors related to weak memory in GPU applications

© 2016 ACM.We present the systematic design of a testing environment that uses stressing and fuzzing to reveal errors in GPU applications that arise due to weak memory effects. We evaluate our approach on seven GPUS spanning three NVIDIA architectures, across ten CUDA applications that use fine-grained concurrency. Our results show that applications that rarely or never exhibit errors related to weak memory when executed natively can readily exhibit these errors when executed in our testing environment. Our testing environment also provides a means to help identify the root causes of such errors, and automatically suggests how to insert fences that harden an application against weak memory bugs. To understand the cost of GPU fences, we benchmark applications with fences provided by the hardening strategy as well as a more conservative, sound fencing strategy

Engineering a static verification tool for GPU kernels

We report on practical experiences over the last 2.5 years related to the engineering of GPUVerify, a static verification tool for OpenCL and CUDA GPU kernels, plotting the progress of GPUVerify from a prototype to a fully functional and relatively efficient analysis tool. Our hope is that this experience report will serve the verification community by helping to inform future tooling efforts. © 2014 Springer International Publishing

Least-squares methods with Poissonian noise: an analysis and a comparison with the Richardson-Lucy algorithm

It is well-known that the noise associated with the collection of an astronomical image by a CCD camera is, in large part, Poissonian. One would expect, therefore, that computational approaches that incorporate this a priori information will be more effective than those that do not. The Richardson-Lucy (RL) algorithm, for example, can be viewed as a maximum-likelihood (ML) method for image deblurring when the data noise is assumed to be Poissonian. Least-squares (LS) approaches, on the other hand, arises from the assumption that the noise is Gaussian with fixed variance across pixels, which is rarely accurate. Given this, it is surprising that in many cases results obtained using LS techniques are relatively insensitive to whether the noise is Poissonian or Gaussian. Furthermore, in the presence of Poisson noise, results obtained using LS techniques are often comparable with those obtained by the RL algorithm. We seek an explanation of these phenomena via an examination of the regularization properties of particular LS algorithms. In addition, a careful analysis of the RL algorithm yields an explanation as to why it is more effective than LS approaches for star-like objects, and why it provides similar reconstructions for extended objects. We finish with a convergence analysis of the RL algorithm. Numerical results are presented throughout the paper. It is important to stress that the subject treated in this paper is not academic. In fact, in comparison with many ML algorithms, the LS algorithms are much easier to use and to implement, often provide faster convergence rates, and are much more flexible regarding the incorporation of constraints on the solution. Consequently, if little to no improvement is gained in the use of an ML approach over an LS algorithm, the latter will often be the preferred approach.Comment: High resolution images are available upon request. submitted to A&

An approximate empirical Bayesian method for large-scale linear-Gaussian inverse problems

We study Bayesian inference methods for solving linear inverse problems, focusing on hierarchical formulations where the prior or the likelihood function depend on unspecified hyperparameters. In practice, these hyperparameters are often determined via an empirical Bayesian method that maximizes the marginal likelihood function, i.e., the probability density of the data conditional on the hyperparameters. Evaluating the marginal likelihood, however, is computationally challenging for large-scale problems. In this work, we present a method to approximately evaluate marginal likelihood functions, based on a low-rank approximation of the update from the prior covariance to the posterior covariance. We show that this approximation is optimal in a minimax sense. Moreover, we provide an efficient algorithm to implement the proposed method, based on a combination of the randomized SVD and a spectral approximation method to compute square roots of the prior covariance matrix. Several numerical examples demonstrate good performance of the proposed method

Optimally shaped terahertz pulses for phase retrieval in a Rydberg atom data register

We employ Optimal Control Theory to discover an efficient information retrieval algorithm that can be performed on a Rydberg atom data register using a shaped terahertz pulse. The register is a Rydberg wave packet with one consituent orbital phase-reversed from the others (the ``marked bit''). The terahertz pulse that performs the decoding algorithm does so by by driving electron probability density into the marked orbital. Its shape is calculated by modifying the target of an optimal control problem so that it represents the direct product of all correct solutions to the algorithm.Comment: 6 pages, 3 figure

Molecular analysis of HLA-DQB1 alleles in childhood common acute lymphoblastic leukaemia.

Epidemiological studies suggest that childhood common acute lymphoblastic leukaemia (c-ALL) may be the rare outcome of early post-natal infection with a common infectious agent. One of the factors that may determine whether a child succumbs to c-ALL is how it responds to the candidate infection. Since immune responses to infection are under the partial control of (human leucocyte antigen) HLA genes, an association between an HLA allele and c-ALL could provide support for an infectious aetiology. To define the limit of c-ALL susceptibility within the HLA region, we have compared HLA-DQB1 allele frequencies in a cohort of 62 children with c-ALL with 76 newborn controls, using group-specific polymerase chain reaction (PCR) amplification, and single-strand conformation polymorphism (SSCP) analysis. We find that a significant excess of children with c-ALL type for DQB1*05 [relative risk (RR): 2.54, uncorrected P=0.038], and a marginal excess with DQB1*0501 (RR: 2.18; P=0.095). Only 3 of the 62 children with c-ALL have the other susceptibility allele, DPB1*0201 as well as DQB1*0501, whereas 15 had one or the other allele. This suggests that HLA-associated susceptibility may be determined independently by at least two loci, and is not due to linkage disequilibrium. The combined relative risk of the two groups of children with DPB1*0201 and/or DQB1*0501 is 2.76 (P=0.0076). Analysis of amino acids encoded by exon 2 of DQB1 reveal additional complexity, with significant (P<0.05) or borderline-significant increases in Gly26, His30, Val57, Glu66-Val67 encoding motifs in c-ALL compared with controls. Since these amino acids are not restricted to DQB1*0501, our results suggest that, as with DPB1, the increased risk of c-ALL associated with DQB1 is determined by specific amino acid encoding motifs rather than by an individual allele. These results also suggest that HLA-associated susceptibility to c-ALL may not be restricted to the region bounded by DPB1 and DQB1

The potential of decision support systems to improve risk assessment for pollen beetle management in winter oilseed rape

BACKGROUNDThe reliance on and extensive use of pyrethroid insecticides have led to pyrethroid resistance in pollen beetle (Meligethes aeneus). Widespread adoption of best practice in pollen beetle management is therefore needed. Decision support systems (DSSs) that identify the risk period(s) for pest migration can help to target monitoring and control efforts, but they must be accurate and labour efficient to gain the support of growers. Weather data and the phenology of pollen beetles in 44 winter oilseed rape crops across England over 4 years were used to compare the performance of two risk management tools: the DSS proPlant expert, which predicts migration risk according to a phenological model and local weather data, and rule-based advice', which depends on crop growth stage and a temperature threshold. RESULTSBoth risk management tools were effective in prompting monitoring that would detect breaches of various control thresholds. However, the DSS more accurately predicted migration start and advised significantly fewer days of migration risk, consultation days and monitoring than did rule-based advice. CONCLUSIONThe proPlant expert DSS reliably models pollen beetle phenology. Use of such a DSS can focus monitoring effort to when it is most needed, facilitate the practical use of thresholds and help to prevent unnecessary insecticide applications and the development of insecticide resistance. (c) 2015 Rothamsted Research Ltd. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry