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Design and Development of Grid Enabled, G2PU Accelerated Java Application (Protein Sequence Study) for Grid Performance Analysis

Subrata Sinha, Smriti Priya Medhi, G.C. Hazarika
2015 Procedia Computer Science  
huge computational power resulting in extensive use of high performance computing (Multi Core Computing, G2PU Computing, CPU-GPU Hybrid computing, Cluster, Grid) models.  ...  advancement in the field of structural biology has generated huge volume of data and analyzing such data is vital to know the hidden truths of life but such analysis is compute intensive in nature and requires  ...  Subrata Chakraborty, Director i/c Centre for Bioinformatics Studies for facilitating with the infrastructure to construct the Grid and perform the experiments. Dr. K Narain, Dy.  ... 
doi:10.1016/j.procs.2015.10.116 fatcat:gkzzqulxczh7jixpp7bq3chmdq

Enabling radiation tolerant heterogeneous GPU-based onboard data processing in space

Fredrik C. Bruhn, Nandinbaatar Tsog, Fabian Kunkel, Oskar Flordal, Ian Troxel
2020 CEAS Space Journal  
Traditional handling and downloading of Big Data from space requires a large onboard mass storage and high bandwidth downlink with a trend towards optical links.  ...  An evaluation of the AMD 14 nm Ryzen APU is presented as a candidate for future advanced onboard processing for space vehicles.  ...  We thank AMD corporation for hardware donations and specifically Mr. Mazda Sabony of AMD for driver support.  ... 
doi:10.1007/s12567-020-00321-9 fatcat:peuy5ito2fhkzb5q6tu73wrzqe

LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing

B. Salami, K. Parasyris, A. Cristal, O. Unsal, X. Martorell, P. Carpenter, R. De La Cruz, L. Bautista, D. Jimenez, C. Alvarez, S. Nabavi, S. Madonar (+31 others)
2020 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)  
The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines.  ...  To support Hybrid GPU/CPU in FTI we extend the implementation of the FTI Protect API call. The function identifies the physical location of the data.  ...  An example using the extended GPU/CPU checkpoint API is presented in Listing 1.  ... 
doi:10.23919/date48585.2020.9116362 dblp:conf/date/0001PCUMCCBJANM20 fatcat:dnwv4t72yfee5ojpnt7agawpuu

LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing [article]

B. Salami, K. Parasyris, A. Cristal, O. Unsal, X. Martorell, P. Carpenter, R. De La Cruz, L. Bautista, D. Jimenez, C. Alvarez, S. Nabavi, S. Madonar (+32 others)
2019 arXiv   pre-print
The LEGaTO project leverages task-based programming models to provide a software ecosystem for Made in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines.  ...  To support Hybrid GPU/CPU in FTI we extend the implementation of the FTI Protect API call. The function identifies the physical location of the data.  ...  An example using the extended GPU/CPU checkpoint API is presented in Listing 1.  ... 
arXiv:1912.01563v1 fatcat:fnaovxczgzbqlbidh6rj5qynuy

High Performance Data Mining Using R on Heterogeneous Platforms

Prabhat Kumar, Berkin Ozisikyilmaz, Wei-Keng Liao, Gokhan Memik, Alok Choudhary
2011 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum  
In this paper, we present a scalable framework aimed at providing a platform for developing and using high performance data mining applications on heterogeneous platforms.  ...  Conventional systems based on general-purpose processors are unable to keep pace with the heavy computational requirements of data mining techniques.  ...  CPU kernels are used for hybrid-execution on a heterogeneous cluster comprising of GPUs and CPUs. For CPU, some kernels are already available in R.  ... 
doi:10.1109/ipdps.2011.329 dblp:conf/ipps/KumarOLMC11 fatcat:op76vbbjobh5dhuh77ur6uvdwy

Enabling EASEY Deployment of Containerized Applications for Future HPC Systems [chapter]

Maximilian Höb, Dieter Kranzlmüller
2020 Lecture Notes in Computer Science  
Our solution builds on a layered software architecture, which offers different mechanisms on each layer for different tasks of tuning, including a workflow management system.  ...  The upcoming exascale era will push the changes in computing architecture from classical CPU-based systems towards hybrid GPU-heavy systems with much higher levels of complexity.  ...  EASEY integration in the layered architecture of HPC systems Compute Nodes Interconnect Persistent Storage GPU CPU Batch System SLURM PBS MPI Dockerized Applications EASEY CLIENT EASEY  ... 
doi:10.1007/978-3-030-50371-0_15 fatcat:etqlvovbtvfazooftb4mar75vq

Enabling EASEY deployment of containerized applications for future HPC systems [article]

Maximilian Höb, Dieter Kranzlmüller
2020 arXiv   pre-print
The solution builds on a layered software architecture, which offers different mechanisms on each layer for different tasks of tuning.  ...  The upcoming exascale era will push the changes in computing architecture from classical CPU-based systems in hybrid GPU-heavy systems with much higher levels of complexity.  ...  EASEY integration in the layered architecture of HPC systems Compute Nodes Interconnect Persistent Storage GPU CPU Batch System SLURM PBS MPI Dockerized Applications EASEY CLIENT EASEY  ... 
arXiv:2004.13373v1 fatcat:wxfjkiuk4be3vklbkbreqnh5km

Feature-based analysis of large-scale spatio-temporal sensor data on hybrid architectures

Joel H. Saltz, George Teodoro, Tony Pan, Lee A.D. Cooper, Jun Kong, Scott Klasky, Tahsin M. Kurc
2013 The international journal of high performance computing applications  
In this paper, we describe middleware system support to take advantage of large clusters of hybrid CPU-GPU nodes to address the data and compute-intensive requirements of feature-based analyses in large  ...  Analysis of large sensor datasets for structural and functional features has applications in many domains, including weather and climate modeling, characterization of subsurface reservoirs, and biomedicine  ...  We have devised a scheduling strategy, called PRIORITY, which uses a sorted queue of (task, data element) tuples based on the relative GPU/CPU speedup expected for each tuple.  ... 
doi:10.1177/1094342013488260 pmid:28496298 pmcid:PMC5423684 fatcat:5wza6w6qnvfqnchi27jpekhohy

Acceleration-as-a-μService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs [article]

Dionysios Diamantopoulos, Raphael Polig, Burkhard Ringlein, Mitra Purandare, Beat Weiss, Christoph Hagleitner, Mark Lantz, Francois Abel
2021 arXiv   pre-print
Existing accelerator techniques for cloud sacrifice the consolidation benefits of microservices. This paper presents CloudiFi, a framework to deploy and compare accelerators as a cloud service.  ...  The evolution of cloud applications into loosely-coupled microservices opens new opportunities for hardware accelerators to improve workload performance.  ...  arXiv:2106.06293v1 [cs.NI] 11 Jun 2021Fig. 2: Overview of CloudiFi: Turning an Infrastructure/Platform into a cloud µService. CPU GPU CPU FPGA GPU Python Nvidia driv.  ... 
arXiv:2106.06293v1 fatcat:jgb2haqsi5b6deg4372nvhdrom

A software-defined architecture for control of IoT Cyberphysical Systems [article]

Ala' Darabseh, Nikolaos M. Freris
2018 arXiv   pre-print
In addition, we propose a middleware layer to encapsulate units and services for time-critical operations in highly dynamic environments.  ...  Based on software-defined principles, we propose a holistic architecture for Cyberphysical Systems (CPS) and Internet of Things (IoT) applications, and highlight the merits pertaining to scalability, flexibility  ...  Dedicated tools for GPU, CPU and memory managements are installed. there is a great number of scheduling algorithms that can be used for this purpose, see for example [21] . 4.  ... 
arXiv:1810.03822v1 fatcat:wtlsi2yrzvdepklg3pkj4kmvue

Hybrid Distributed Real Time Scheduling Algorithm

A.Prashanth Rao, Dr.A. Govardhan, C. Venu Gopal
2011 GSTF International Journal on Computing  
A method for maintaining quality of service in game servers with excessive users is often done by increasing the number of game servers.  ...  In this paper, we argue that graphic processor (GPU) working in parallel with local central processor (CPU) inside a machine can be a good candidate for reducing the workload, before attempting to distribute  ...  Elapsed Time case service service (ms) number running on running on 1 CPU CPU 1.209 2 GPU CPU 0.046 3 CPU GPU 1.237 4 GPU GPU 0.074 Suntorn (b) Score ranking service (c) User  ... 
doi:10.5176/2010-2283_1.2.54 fatcat:ag3dlclouzgd7krhubsj55scoq

Region templates: Data representation and management for high-throughput image analysis

George Teodoro, Tony Pan, Tahsin Kurc, Jun Kong, Lee Cooper, Scott Klasky, Joel Saltz
2014 Parallel Computing  
The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing  ...  hybrid computing nodes.  ...  computation; (iii) GPUs + CPUs (1L) uses the CPUs and GPUs in coordination, but the application stages are represented as a single task that bundles all the internal operations; (iii) GPUs + CPUs (2L)  ... 
doi:10.1016/j.parco.2014.09.003 pmid:26139953 pmcid:PMC4484879 fatcat:4miblqmyyzad5bdcvmzxngv2oy

HyPar-Flow: Exploiting MPI and Keras for Scalable Hybrid-Parallel DNN Training with TensorFlow [chapter]

Ammar Ahmad Awan, Arpan Jain, Quentin Anthony, Hari Subramoni, Dhabaleswar K. Panda
2020 Lecture Notes in Computer Science  
Four major problems we focus on are: 1) defining a notion of a distributed model across processes, 2) implementing forward/back-propagation across process boundaries that requires explicit communication  ...  To address these problems, we create HyPar-Flow-a model-size and model-type agnostic, scalable, practical, and user-transparent system for hybrid-parallel training by exploiting MPI, Keras, and TensorFlow  ...  Model and Hybrid-Parallelism: Data-parallelism works for models that can fit completely inside the memory of a single GPU/CPU.  ... 
doi:10.1007/978-3-030-50743-5_5 fatcat:dsi62u6lbjanjdgx2dagb7fwmm

Region Templates: Data Representation and Management for Large-Scale Image Analysis [article]

George Teodoro, Tony Pan, Tahsin Kurc, Jun Kong, Lee Cooper, Scott Klasky, Joel Saltz
2014 arXiv   pre-print
A number of optimizations for hybrid machines are available in our runtime system, including performance-aware scheduling for maximizing utilization of computing devices and techniques to reduce impact  ...  In this paper, we introduce a region template abstraction for the efficient management of common data types used in analysis of large datasets of high resolution images on clusters of hybrid computing  ...  computation; (iii) GPUs + CPUs (1L) uses the CPUs and GPUs in coordination, but the application stages are represented as a single task that bundles all the internal operations; (iii) GPUs + CPUs (2L)  ... 
arXiv:1405.7958v1 fatcat:doemcxj4djhmnplhvhkimy2x3q

Patterns for High Performance Multiscale Computing

S. Alowayyed, T. Piontek, J.L. Suter, O. Hoenen, D. Groen, O. Luk, B. Bosak, P. Kopta, K. Kurowski, O. Perks, K. Brabazon, V. Jancauskas (+3 others)
2018 Future generations computer systems  
Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific  ...  three multiscale models (two MCPs). • We demonstrate how the software automates resource selection and load balancing. a b s t r a c t We describe our Multiscale Computing Patterns software for High Performance  ...  SA acknowledges funding from King Abdulaziz City for Science and Technology (KACST), Saudi Arabia.  ... 
doi:10.1016/j.future.2018.08.045 fatcat:b5toh5yu4vb3xbv5mdud2lzoeu
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