scholarly journals Predicting Runtime in HPC Environments for an Efficient Use of Computational Resources

2021 ◽  
Author(s):  
Mariza Ferro ◽  
Vinicius P. Klôh ◽  
Matheus Gritz ◽  
Vitor de Sá ◽  
Bruno Schulze
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
High Performance ◽  
Performance Metrics ◽  
Scientific Applications ◽  
Computing Systems ◽  
Computational Resources ◽  
Performance Computing

Understanding the computational impact of scientific applications on computational architectures through runtime should guide the use of computational resources in high-performance computing systems. In this work, we propose an analysis of Machine Learning (ML) algorithms to gather knowledge about the performance of these applications through hardware events and derived performance metrics. Nine NAS benchmarks were executed and the hardware events were collected. These experimental results were used to train a Neural Network, a Decision Tree Regressor and a Linear Regression focusing on predicting the runtime of scientific applications according to the performance metrics.

scholarly journals An Architecture for Developing Cyber Environments using Multiple HPC Infrastructures for e-Science Applications

2015 ◽  
Author(s):  
Felipe Maciel ◽  
Carina Oliveira ◽  
Renato Juaçaba Neto ◽  
João Alencar ◽  
Paulo Rego ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Scientific Applications ◽  
Computational Resources ◽  
Performance Computing

In this paper, we propose a novel architecture to allow the implementation of a cyber environment composed of different High Performance Computing (HPC) infrastructures (i.e., clusters, grids and clouds). To access this cyber environment, scientific researchers do not have to become computer experts. In particular, we assume that scientific researchers provide a description of the problem as an input to the cyber environment and then get their results without being responsible for managing the computational resources. We provide a prototype of the architecture and introduce an evaluation which studies a real workload of scientific applications executions. The results show the advantages of the proposed architecture. Besides, we highlight this work provides guidelines for developing cyber environments focused on e-Science.

Estimating the overhead and coupling of scientific computing clusters

SIMULATION ◽  
2021 ◽  
pp. 003754972110641
Author(s):  
Aurelio Vivas ◽  
Harold Castro
Keyword(s):  
High Performance ◽  
Scientific Computing ◽  
Virtual Machines ◽  
Building Blocks ◽  
Scientific Applications ◽  
Computing Systems ◽  
The Third ◽  
Cluster Of Workstations ◽  
Computer Aided ◽  
Performance Computing

Since simulation became the third pillar of scientific research, several forms of computers have become available to drive computer aided simulations, and nowadays, clusters are the most popular type of computers supporting these tasks. For instance, cluster settings, such as the so-called supercomputers, cluster of workstations (COW), cluster of desktops (COD), and cluster of virtual machines (COV) have been considered in literature to embrace a variety of scientific applications. However, those scientific applications categorized as high-performance computing (HPC) are conceptually restricted to be addressed only by supercomputers. In this aspect, we introduce the notions of cluster overhead and cluster coupling to assess the capacity of non-HPC systems to handle HPC applications. We also compare the cluster overhead with an existing measure of overhead in computing systems, the total parallel overhead, to explain the correctness of our methodology. The evaluation of capacity considers the seven dwarfs of scientific computing, which are well-known, scientific computing building blocks considered in the development of HPC applications. The evaluation of these building blocks provides insights regarding the strengths and weaknesses of non-HPC systems to deal with future HPC applications developed with one or a combination of these algorithmic building blocks.

On the trade‐off of mixing scientific applications on capacity high‐performance computing systems

2013 ◽  
Vol 7 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Ana Jokanovic ◽  
Jose Carlos Sancho ◽  
German Rodriguez ◽  
Cyriel Minkenberg ◽  
Ramon Beivide ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Scientific Applications ◽  
Computing Systems ◽  
Trade Off ◽  
Performance Computing

GPU Scaling

2014 ◽  
Vol 9 (4) ◽  
pp. 13-23
Author(s):  
Yaser Jararweh ◽  
Moath Jarrah ◽  
Abdelkader Bousselham
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Gpu Computing ◽  
State Of The Art ◽  
Computing Systems ◽  
Current State ◽  
Viable Solution ◽  
Order Of Magnitude ◽  
Computational Resources ◽  
Performance Computing

Current state-of-the-art GPU-based systems offer unprecedented performance advantages through accelerating the most compute-intensive portions of applications by an order of magnitude. GPU computing presents a viable solution for the ever-increasing complexities in applications and the growing demands for immense computational resources. In this paper the authors investigate different platforms of GPU-based systems, starting from the Personal Supercomputing (PSC) to cloud-based GPU systems. The authors explore and evaluate the GPU-based platforms and the authors present a comparison discussion against the conventional high performance cluster-based computing systems. The authors' evaluation shows potential advantages of using GPU-based systems for high performance computing applications while meeting different scaling granularities.

GPU Scaling

2016 ◽  
pp. 2373-2384
Author(s):  
Yaser Jararweh ◽  
Moath Jarrah ◽  
Abdelkader Bousselham
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Gpu Computing ◽  
State Of The Art ◽  
Computing Systems ◽  
Current State ◽  
Viable Solution ◽  
Order Of Magnitude ◽  
Computational Resources ◽  
Performance Computing

Current state-of-the-art GPU-based systems offer unprecedented performance advantages through accelerating the most compute-intensive portions of applications by an order of magnitude. GPU computing presents a viable solution for the ever-increasing complexities in applications and the growing demands for immense computational resources. In this paper the authors investigate different platforms of GPU-based systems, starting from the Personal Supercomputing (PSC) to cloud-based GPU systems. The authors explore and evaluate the GPU-based platforms and the authors present a comparison discussion against the conventional high performance cluster-based computing systems. The authors' evaluation shows potential advantages of using GPU-based systems for high performance computing applications while meeting different scaling granularities.

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

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