HDF Cloud – Helmholtz Data Federation Cloud Resources at the Jülich Supercomputing Centre

The HDF Cloud is an OpenStack based infrastructure-as-a-service (IaaS) environment operated by Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. It has been installed predominantly to support challenging data use cases within the Helmholtz Association’s strategic initiative Helmholtz Data Federation (HDF). To this end, it has been connected to one of the central storage resources of JSC, the DATA file system that is also available on the high-performance computing systems

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 Helmholtz Analytics Toolkit (Heat) and its role in the landscape of massively-parallel scientific Python

When it comes to enhancing exploitation of massive data, machine learning methods are at the forefront of researchers’ awareness. Much less so is the need for, and the complexity of, applying these techniques efficiently across large-scale, memory-distributed data volumes. In fact, these aspects typical for the handling of massive data sets pose major challenges to the vast majority of research communities, in particular to those without a background in high-performance computing. Often, the standard approach involves breaking up and analyzing data in smaller chunks; this can be inefficient and prone to errors, and sometimes it might be inappropriate at all because the context of the overall data set can get lost. The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python. The main objective is to make memory-intensive data analysis possible across various fields of research ---in particular for domain scientists being non-experts in traditional high-performance computing who nevertheless need to tackle data analytics problems going beyond the capabilities of a single workstation. The development of this interdisciplinary, general-purpose, and open-source scientific Python library started in 2018 and is based on collaboration of three institutions (German Aerospace Center DLR, Forschungszentrum Jülich FZJ, Karlsruhe Institute of Technology KIT) of the Helmholtz Association. The pillars of its development are... - ...to enable memory distribution of n-dimensional arrays, - to adopt PyTorch as process-local compute engine (hence supporting GPU-acceleration), - to provide memory-distributed (i.e., multi-node, multi-GPU) array operations and algorithms, optimizing asynchronous MPI-communication (based on mpi4py) under the hood, and - to wrap functionalities in NumPy- or scikit-learn-like API to achieve porting of existing applications with minimal changes and to enable the usage by non-experts in HPC. In this talk we will give an illustrative overview on the current features and capabilities of our library. Moreover, we will discuss its role in the existing ecosystem of distributed computing in Python, and we will address technical and operational challenges in further development