Advanced ab initio materials simulations face growing challenges as increasing systems and phenomena complexity requires higher accuracy, driving up computational demands. Quantum many-body GW methods are state-of-the-art for treating electronic excited states and couplings but often hindered due to the costly numerical complexity. Here, we present innovative implementations of advanced GW methods within the BerkeleyGW package, enabling large-scale simulations on Frontier and Aurora exascale platforms. Our approach demonstrates exceptional versatility for complex heterogeneous systems with up to 17,574 atoms, along with achieving true performance portability across GPU architectures. We demonstrate excellent strong and weak scaling to thousands of nodes, reaching double-precision core-kernel performance of 1.069 ExaFLOP/s on Frontier (9,408 nodes) and 707.52 PetaFLOP/s on Aurora (9,600 nodes), corresponding to 59.45% and 48.79% of peak, respectively. Our work demonstrates a breakthrough in utilizing exascale computing for quantum materials simulations, delivering unprecedented predictive capabilities for rational designs of future quantum technologies.

The problem of record linkage is to cluster the records from several data sources such that each cluster has all the records belonging to one and only one entity. Record linkage has applications in a wide variety of domains including public health, law enforcement, fraud detection, biology, and transportation. Given the typically vast sizes of datasets, existing algorithms suffer from very long runtimes. Hence, it is essential to develop novel algorithms tailored to address this issue. Blocking is a popular technique employed to speed up record linkage algorithms. In this paper, we employ a blocking technique that is based on Soundex encoding. Soundex index is a method of coding names based on their pronunciation rather than their spelling. Soundex has been traditionally used only as a distance metric. In this paper, we show how to use Soundex as a blocking technique. To the best of our knowledge, no one else has done it in the past. In fact, we introduce two novel blocking approaches that utilize Soundex encoding: One stage Soundex blocking and Two stage Soundex blocking.Our approaches exhibit superior linkage performance compared to the state-of-the-art record linkage algorithms, as evidenced by higher F-1 scores and reduced linkage times. The proposed blocking approaches prove to be highly effective.

We develop an algorithm for single-image superresolution of remotely sensed data based on the discrete shearlet transform. The shearlet transform extracts directional features of signals and is known to provide near-optimally sparse representations for a broad class of images. This often leads to superior performance in edge detection and image representation when compared with isotropic frames. We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SMEs). Our algorithm is compared with a variety of single-image superresolution methods, including wavelet SME superresolution. Our numerical results demonstrate competitive performance in terms of peak-signal-to-noise ratio and structural similarity index metric.