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As supercomputers continue to grow in scale and capabilities, it is becoming increasingly difficult to isolate processor and system level causes of performance degradation. Over the last several years, a significant number of performance…
Missing data can lead to inefficiencies and biases in analyses, in particular when data are missing not at random (MNAR). It is thus vital to understand and correctly identify the missing data mechanism. Recovering missing values through a…
dtaianomaly is an open-source Python library for time series anomaly detection, designed to bridge the gap between academic research and real-world applications. Our goal is to (1) accelerate the development of novel state-of-the-art…
The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward…
This paper presents a systematic review of Python packages with a focus on time series analysis. The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an…
Asymmetric multicore processors (AMPs) couple high-performance big cores and low-power small cores with the same instruction-set architecture but different features, such as clock frequency or microarchitecture. Previous work has shown that…
A recent study showed that more than 70% of researchers fail to reproduce their peers's experiments and more than half fail to reproduce their own experiments. Obviously, from a perspective of scientific quality this is a more than…
Numba is a game-changing compiler for high-performance computing with Python. It produces machine code that runs outside of the single-threaded Python interpreter and that fully utilizes the resources of modern CPUs. This means support for…
Python is one of the fastest-growing programming languages and currently ranks as the top language in many lists, even recently overtaking JavaScript as the top language on GitHub. Given its importance in data science and machine learning,…
Parallel processing is considered as todays and future trend for improving performance of computers. Computing devices ranging from small embedded systems to big clusters of computers rely on parallelizing applications to reduce execution…
The amazing advances being made in the fields of machine and deep learning are a highlight of the Big Data era for both enterprise and research communities. Modern applications require resources beyond a single node's ability to provide.…
As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using…
We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface (MPI), we provide parallel implementations of many commonly used…
Data races, a major source of bugs in concurrent programs, can result in loss of manpower and time as well as data loss due to system failures. OpenMP, the de facto shared memory parallelism framework used in the HPC community, also suffers…
We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to programmatically label and build training data. SPEAR…
SfePy (Simple finite elements in Python) is a software for solving various kinds of problems described by partial differential equations in one, two or three spatial dimensions by the finite element method. Its source code is mostly (85\%)…
Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment. Data curation and pre-processing of medical images are critical…
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. Despite growing research in NA, there lacks a comprehensive…
The theory of divide-and-conquer parallelization has been well-studied in the past, providing a solid basis upon which to explore different approaches to the parallelization of merge sort in Python. Python's simplicity and extensive…
We present Cobaya, a general-purpose Bayesian analysis code aimed at models with complex internal interdependencies. Without the need for specific code by the user, interdependencies between different stages of a model pipeline are…