Exploring Scientific Application Performance Using Large Scale Object Storage

One of the major performance and scalability bottlenecks in large scientific applications is parallel reading and writing to supercomputer I/O systems. The usage of parallel file systems and consistency requirements of POSIX, that all the traditional HPC parallel I/O interfaces adhere to, pose limitations to the scalability of scientific applications. Object storage is a widely used storage technology in cloud computing and is more frequently proposed for HPC workload to address and improve the current scalability and performance of I/O in scientific applications. While object storage is a promising technology, it is still unclear how scientific applications will use object storage and what the main performance benefits will be. This work addresses these questions, by emulating an object storage used by a traditional scientific application and evaluating potential performance benefits. We show that scientific applications can benefit from the usage of object storage on large scales.Comment: Preprint submitted to WOPSSS workshop at ISC 201

NVIDIA Tensor Core Programmability, Performance & Precision

The NVIDIA Volta GPU microarchitecture introduces a specialized unit, called "Tensor Core" that performs one matrix-multiply-and-accumulate on 4x4 matrices per clock cycle. The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed precision. In this paper, we investigate current approaches to program NVIDIA Tensor Cores, their performances and the precision loss due to computation in mixed precision. Currently, NVIDIA provides three different ways of programming matrix-multiply-and-accumulate on Tensor Cores: the CUDA Warp Matrix Multiply Accumulate (WMMA) API, CUTLASS, a templated library based on WMMA, and cuBLAS GEMM. After experimenting with different approaches, we found that NVIDIA Tensor Cores can deliver up to 83 Tflops/s in mixed precision on a Tesla V100 GPU, seven and three times the performance in single and half precision respectively. A WMMA implementation of batched GEMM reaches a performance of 4 Tflops/s. While precision loss due to matrix multiplication with half precision input might be critical in many HPC applications, it can be considerably reduced at the cost of increased computation. Our results indicate that HPC applications using matrix multiplications can strongly benefit from using of NVIDIA Tensor Cores.Comment: This paper has been accepted by the Eighth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES) 201

PolyPIC: the Polymorphic-Particle-in-Cell Method for Fluid-Kinetic Coupling

Particle-in-Cell (PIC) methods are widely used computational tools for fluid and kinetic plasma modeling. While both the fluid and kinetic PIC approaches have been successfully used to target either kinetic or fluid simulations, little was done to combine fluid and kinetic particles under the same PIC framework. This work addresses this issue by proposing a new PIC method, PolyPIC, that uses polymorphic computational particles. In this numerical scheme, particles can be either kinetic or fluid, and fluid particles can become kinetic when necessary, e.g. particles undergoing a strong acceleration. We design and implement the PolyPIC method, and test it against the Landau damping of Langmuir and ion acoustic waves, two stream instability and sheath formation. We unify the fluid and kinetic PIC methods under one common framework comprising both fluid and kinetic particles, providing a tool for adaptive fluid-kinetic coupling in plasma simulations.Comment: Submitted to Frontier

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Mitochondrial physiology

As the knowledge base and importance of mitochondrial physiology to evolution, health and disease expands, the necessity for harmonizing the terminology concerning mitochondrial respiratory states and rates has become increasingly apparent. The chemiosmotic theory establishes the mechanism of energy transformation and coupling in oxidative phosphorylation. The unifying concept of the protonmotive force provides the framework for developing a consistent theoretical foundation of mitochondrial physiology and bioenergetics. We follow the latest SI guidelines and those of the International Union of Pure and Applied Chemistry (IUPAC) on terminology in physical chemistry, extended by considerations of open systems and thermodynamics of irreversible processes. The concept-driven constructive terminology incorporates the meaning of each quantity and aligns concepts and symbols with the nomenclature of classical bioenergetics. We endeavour to provide a balanced view of mitochondrial respiratory control and a critical discussion on reporting data of mitochondrial respiration in terms of metabolic flows and fluxes. Uniform standards for evaluation of respiratory states and rates will ultimately contribute to reproducibility between laboratories and thus support the development of data repositories of mitochondrial respiratory function in species, tissues, and cells. Clarity of concept and consistency of nomenclature facilitate effective transdisciplinary communication, education, and ultimately further discovery

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems

Computational intensive applications such as pattern recognition, and natural language processing, are increasingly popular on HPC systems. Many of these applications use deep-learning, a branch of machine learning, to determine the weights of artificial neural network nodes by minimizing a loss function. Such applications depend heavily on dense matrix multiplications, also called tensorial operations. The use of Graphics Processing Unit (GPU) has considerably speeded up deep-learning computations, leading to a Renaissance of the artificial neural network. Recently, the NVIDIA Volta GPU and the Google Tensor Processing Unit (TPU) have been specially designed to support deep-learning workloads. New programming models have also emerged for convenient expression of tensorial operations and deep-learning computational paradigms. An example of such new programming frameworks is TensorFlow, an open-source deep-learning library released by Google in 2015. TensorFlow expresses algorithms as a computational graph where nodes represent operations and edges between nodes represent data flow. Multi-dimensional data such as vectors and matrices which flows between operations are called Tensors. For this reason, computation problems need to be expressed as a computational graph. In particular, TensorFlow supports distributed computation with flexible assignment of operation and data to devices such as GPU and CPU on different computing nodes. Computation on devices are based on optimized kernels such as MKL, Eigen and cuBLAS. Inter-node communication can be through TCP and RDMA. This work attempts to evaluate the usability and expressiveness of the TensorFlow programming model for traditional HPC problems. As an illustration, we prototyped a distributed block matrix multiplication for large dense matrices which cannot be co-located on a single device and a Conjugate Gradient (CG) solver. We evaluate the difficulty of expressing traditional HPC algorithms using computational graphs and study the scalability of distributed TensorFlow on accelerated systems. Our preliminary result with distributed matrix multiplication shows that distributed computation on TensorFlow is extremely scalable. This study provides an initial investigation of new emerging programming models for HPC.Published in Proceedings of the 5th International Conference on Exascale Applications and Software. Edinburgh: The University of Edinburgh (2018), ISBN: 978-0-9926615-3-3, pp.34, Published under license CC BY-ND 4.0.</p

An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems

Computational intensive applications such as pattern recognition, and natural language processing, are increasingly popular on HPC systems. Many of these applications use deep-learning, a branch of machine learning, to determine the weights of artificial neural network nodes by minimizing a loss function. Such applications depend heavily on dense matrix multiplications, also called tensorial operations. The use of Graphics Processing Unit (GPU) has considerably speeded up deep-learning computations, leading to a Renaissance of the artificial neural network. Recently, the NVIDIA Volta GPU and the Google Tensor Processing Unit (TPU) have been specially designed to support deep-learning workloads. New programming models have also emerged for convenient expression of tensorial operations and deep-learning computational paradigms. An example of such new programming frameworks is TensorFlow, an open-source deep-learning library released by Google in 2015. TensorFlow expresses algorithms as a computational graph where nodes represent operations and edges between nodes represent data flow. Multi-dimensional data such as vectors and matrices which flows between operations are called Tensors. For this reason, computation problems need to be expressed as a computational graph. In particular, TensorFlow supports distributed computation with flexible assignment of operation and data to devices such as GPU and CPU on different computing nodes. Computation on devices are based on optimized kernels such as MKL, Eigen and cuBLAS. Inter-node communication can be through TCP and RDMA. This work attempts to evaluate the usability and expressiveness of the TensorFlow programming model for traditional HPC problems. As an illustration, we prototyped a distributed block matrix multiplication for large dense matrices which cannot be co-located on a single device and a Conjugate Gradient (CG) solver. We evaluate the difficulty of expressing traditional HPC algorithms using computational graphs and study the scalability of distributed TensorFlow on accelerated systems. Our preliminary result with distributed matrix multiplication shows that distributed computation on TensorFlow is extremely scalable. This study provides an initial investigation of new emerging programming models for HPC.Published in Proceedings of the 5th International Conference on Exascale Applications and Software. Edinburgh: The University of Edinburgh (2018), ISBN: 978-0-9926615-3-3, pp.34, Published under license CC BY-ND 4.0.</p

An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems

Computational intensive applications such as pattern recognition, and natural language processing, are increasingly popular on HPC systems. Many of these applications use deep-learning, a branch of machine learning, to determine the weights of artificial neural network nodes by minimizing a loss function. Such applications depend heavily on dense matrix multiplications, also called tensorial operations. The use of Graphics Processing Unit (GPU) has considerably speeded up deep-learning computations, leading to a Renaissance of the artificial neural network. Recently, the NVIDIA Volta GPU and the Google Tensor Processing Unit (TPU) have been specially designed to support deep-learning workloads. New programming models have also emerged for convenient expression of tensorial operations and deep-learning computational paradigms. An example of such new programming frameworks is TensorFlow, an open-source deep-learning library released by Google in 2015. TensorFlow expresses algorithms as a computational graph where nodes represent operations and edges between nodes represent data flow. Multi-dimensional data such as vectors and matrices which flows between operations are called Tensors. For this reason, computation problems need to be expressed as a computational graph. In particular, TensorFlow supports distributed computation with flexible assignment of operation and data to devices such as GPU and CPU on different computing nodes. Computation on devices are based on optimized kernels such as MKL, Eigen and cuBLAS. Inter-node communication can be through TCP and RDMA. This work attempts to evaluate the usability and expressiveness of the TensorFlow programming model for traditional HPC problems. As an illustration, we prototyped a distributed block matrix multiplication for large dense matrices which cannot be co-located on a single device and a Conjugate Gradient (CG) solver. We evaluate the difficulty of expressing traditional HPC algorithms using computational graphs and study the scalability of distributed TensorFlow on accelerated systems. Our preliminary result with distributed matrix multiplication shows that distributed computation on TensorFlow is extremely scalable. This study provides an initial investigation of new emerging programming models for HPC.Published in Proceedings of the 5th International Conference on Exascale Applications and Software. Edinburgh: The University of Edinburgh (2018), ISBN: 978-0-9926615-3-3, pp.34, Published under license CC BY-ND 4.0.</p

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents