Static analyses play a fundamental role during compilation: they discover facts that are true in all executions of the code being compiled, and then these facts are used to justify optimizations and diagnostics. Each static analysis is based on a collection of abstract transformers that provide abstract semantics for the concrete instructions that make up a program. It can be challenging to implement abstract transformers that are sound, precise, and efficient—and in fact both LLVM and GCC have suffered from miscompilations caused by unsound abstract transformers. Moreover, even after more than 20 years of development, LLVM lacks abstract transformers for hundreds of instructions in its intermediate representation (IR). We developed NiceToMeetYou : a program synthesis framework for abstract transformers that are aimed at the kinds of non-relational integer abstract domains that are heavily used by today’s production compilers. It exploits a simple but novel technique for breaking the synthesis problem into parts: each of our transformers is the meet of a collection of simpler, sound transformers that are synthesized such that each new piece fills a gap in the precision of the final transformer. Our design point is bulk automation: no sketches are required. Transformers are verified by lowering to a previously-created SMT dialect of MLIR. Each of our synthesized transformers is provably sound and some (17 %) are more precise than those provided by LLVM.

Graph convolutional networks leverage both graph structures and features on nodes and edges for improved learning performance in comparison with classical machine learning approaches. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing graph learning without features. Incorporating graph features brings the challenge of efficient feature representation and balancing the contribution of topology and features in learning. In this work, we present our design of a simulated network of spiking neurons to perform semi-supervised learning on graph data using both the graph structure and the node features. We explore various design choices, present preliminary results, and discuss the opportunities for using neuromorphic computers for this task in the future.

To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution's record trail and invisibly attaches it to the workflow's output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation.

The use of containerization technology in high performance computing (HPC) workflows has substantially increased recently because it makes workflows much easier to develop and deploy. Although many HPC workflows include multiple data and multiple applications, they have traditionally all been bundled together into one monolithic container. This hinders the ability to trace the thread of execution, thus preventing scientists from establishing data provenance, or having workflow reproducibility. To provide a solution to this problem we extend the functionality of a popular HPC container runtime, Singularity. We implement both the ability to compose fine-grained containerized workflows and execute these workflows within the Singularity runtime with automatic metadata collection. Specifically, the new functionality collects a record trail of execution and creates data provenance. The use of our augmented Singularity is demonstrated with an earth science workflow, SOMOSPIE. The workflow is composed via our augmented Singularity which creates fine-grained containers and collects the metadata to trace, explain, and reproduce the prediction of soil moisture at a fine resolution.

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.