596 research outputs found
zfit: scalable pythonic fitting
Statistical modeling is a key element in many scientific fields and
especially in High-Energy Physics (HEP) analysis. The standard framework to
perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings
that are only loosely integrated into the scientific Python ecosystem. In this
paper, zfit, a new alternative to RooFit written in pure Python, is presented.
Most of all, zfit provides a well defined high-level API and workflow for
advanced model building and fitting, together with an implementation on top of
TensorFlow, allowing a transparent usage of CPUs and GPUs. It is designed to be
extendable in a very simple fashion, allowing the usage of cutting-edge
developments from the scientific Python ecosystem in a transparent way. The
main features of zfit are introduced, and its extension to data analysis,
especially in the context of HEP experiments, is discussed.Comment: 12 pages, 2 figure
PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation
High-performance computing has recently seen a surge of interest in
heterogeneous systems, with an emphasis on modern Graphics Processing Units
(GPUs). These devices offer tremendous potential for performance and efficiency
in important large-scale applications of computational science. However,
exploiting this potential can be challenging, as one must adapt to the
specialized and rapidly evolving computing environment currently exhibited by
GPUs. One way of addressing this challenge is to embrace better techniques and
develop tools tailored to their needs. This article presents one simple
technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL,
two open-source toolkits that support this technique.
In introducing PyCUDA and PyOpenCL, this article proposes the combination of
a dynamic, high-level scripting language with the massive performance of a GPU
as a compelling two-tiered computing platform, potentially offering significant
performance and productivity advantages over conventional single-tier, static
systems. The concept of RTCG is simple and easily implemented using existing,
robust infrastructure. Nonetheless it is powerful enough to support (and
encourage) the creation of custom application-specific tools by its users. The
premise of the paper is illustrated by a wide range of examples where the
technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie
Somoclu: An Efficient Parallel Library for Self-Organizing Maps
Somoclu is a massively parallel tool for training self-organizing maps on
large data sets written in C++. It builds on OpenMP for multicore execution,
and on MPI for distributing the workload across the nodes in a cluster. It is
also able to boost training by using CUDA if graphics processing units are
available. A sparse kernel is included, which is useful for high-dimensional
but sparse data, such as the vector spaces common in text mining workflows.
Python, R and MATLAB interfaces facilitate interactive use. Apart from fast
execution, memory use is highly optimized, enabling training large emergent
maps even on a single computer.Comment: 26 pages, 9 figures. The code is available at
https://peterwittek.github.io/somoclu
HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
To cope with the rapid growth in available data, the efficiency of data
analysis and machine learning libraries has recently received increased
attention. Although great advancements have been made in traditional
array-based computations, most are limited by the resources available on a
single computation node. Consequently, novel approaches must be made to exploit
distributed resources, e.g. distributed memory architectures. To this end, we
introduce HeAT, an array-based numerical programming framework for large-scale
parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch
as a node-local eager execution engine and distributes the workload on
arbitrarily large high-performance computing systems via MPI. It provides both
low-level array computations, as well as assorted higher-level algorithms. With
HeAT, it is possible for a NumPy user to take full advantage of their available
resources, significantly lowering the barrier to distributed data analysis.
When compared to similar frameworks, HeAT achieves speedups of up to two orders
of magnitude.Comment: 10 pages, 8 figures, 5 listings, 1 tabl
HeAT – a Distributed and GPU-accelerated Tensor Framework for Data Analytics
In order to cope with the exponential growth in available data, the efficiency of data analysis and machine learning libraries have recently received increased attention. Although corresponding array-based numerical kernels have been significantly improved, most are limited by the resources available on a single computational node. Consequently, kernels must exploit distributed resources, e.g., distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload via MPI on arbitrarily large high-performance computing systems. It provides both low-level array-based computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take advantage of their available resources, significantly lowering the barrier to distributed data analysis. Compared with applications written in similar frameworks, HeAT achieves speedups of up to two orders of magnitude
The DeepHealth Toolkit: A key European free and open-source software for deep learning and computer vision ready to exploit heterogeneous HPC and cloud architectures
At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.
iThis chapter describes work undertaken in the context of the DeepHealth project, “Deep-Learning and HPC to Boost Biomedical Applications for Health”, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111.Peer Reviewed"Article signat per 19 autors/es: Marco Aldinucci, David Atienza, Federico Bolelli, Mónica Caballero, Iacopo Colonnelli, José Flich, Jon A. Gómez, David González, Costantino Grana, Marco Grangetto, Simone Leo, Pedro López, Dana Oniga, Roberto Paredes, Luca Pireddu, Eduardo Quiñones, Tatiana Silva, Enzo Tartaglione & Marina Zapater "Postprint (author's final draft
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