By Yu-hsuan Shih, Garrett Wright, Joakim Andén, Johannes Blaschke, Alex H. Barnett

Nonuniform fast Fourier transforms dominate the computational cost in many applications including image reconstruction and signal processing. We thus present a general-purpose GPU-based CUDA library for type 1 (nonuniform to uniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3, in single or double precision. It achieves high performance for a given user-requested accuracy, regardless of the distribution of nonuniform points, via cache-aware point reordering, and load-balanced blocked spreading in shared memory. At low accuracies, this gives on-GPU throughputs around 10e9 nonuniform points per second, and (even including host-device transfer) is typically 4-10x faster than the latest parallel CPU code FINUFFT (at 28 threads). It is competitive with two established GPU codes, being up to 90x faster at high accuracy and/or type 1 clustered point distributions. Finally we demonstrate a 5-12x speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10e-12 accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.

Read the paper: https://doi.org/10.48550/arXiv.2102.08463

By Giovanni Petri, Sebastian Musslick, Biswadip Dey, Kayhan Özcimder, David Turner, Nesreen K. Ahmed, Theodore L. Willke & Jonathan D. Cohen

The ability to learn new tasks and generalize to others is a remarkable characteristic of both human brains and recent artificial intelligence systems. The ability to perform multiple tasks simultaneously is also a key characteristic of parallel architectures, as is evident in the human brain and exploited in traditional parallel architectures. Here we show that these two characteristics reflect a fundamental tradeoff between interactive parallelism, which supports learning and generalization, and independent parallelism, which supports processing efficiency through concurrent multitasking. Although the maximum number of possible parallel tasks grows linearly with network size, under realistic scenarios their expected number grows sublinearly. Hence, even modest reliance on shared representations, which support learning and generalization, constrains the number of parallel tasks. This has profound consequences for understanding the human brain’s mix of sequential and parallel capabilities, as well as for the development of artificial intelligence systems that can optimally manage the tradeoff between learning and processing efficiency.

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Read the paper: https://www.nature.com/articles/s41567-021-01170-x

By Yuki Sugimoto, Francine R. Camacho, Shuo Wang, Pranatchareeya Chankhamjon, Arman Odabas, Abhishek Biswas, Philip D. Jeffrey, Mohamed S. Donia

The human microbiome is a vast and complex ecosystem, teeming with microbial life that influences our health in profound ways. While correlations between microbiome composition and disease have been widely studied, the molecular mechanisms behind these relationships remain elusive. One promising avenue for exploration lies in the small molecules produced by these microbes—compounds that mediate interactions both among microbes and between microbes and their human hosts.

In this groundbreaking study, the authors introduce MetaBGC, a hybrid computational and synthetic biology strategy designed to uncover biosynthetic gene clusters (BGCs) directly from metagenomic sequencing data. These clusters encode the machinery for producing bioactive small molecules, including antibiotics and other therapeutics.

Read the paper: https://www.science.org/doi/10.1126/science.aax9176

By Kindel, A. T., Bansal, V., Catena, K. D., Hartshorne, T. H., Jaeger, K., Koffman, D., McLanahan, S., Phillips, M., Rouhani, S., Vinh, R., & Salganik, M. J.

Researchers rely on metadata systems to prepare data for analysis. As the complexity of data sets increases and the breadth of data analysis practices grow, existing metadata systems can limit the efficiency and quality of data preparation. This article describes the redesign of a metadata system supporting the Fragile Families and Child Wellbeing Study on the basis of the experiences of participants in the Fragile Families Challenge. The authors demonstrate how treating metadata as data (i.e., releasing comprehensive information about variables in a format amenable to both automated and manual processing) can make the task of data preparation less arduous and less error prone for all types of data analysis. The authors hope that their work will facilitate new applications of machine-learning methods to longitudinal surveys and inspire research on data preparation in the social sciences. The authors have open-sourced the tools they created so that others can use and improve them.

Read the paper: https://doi.org/10.1177/2378023118817378