scholarly journals High-performance method for identification of super enhancers from ChIP-Seq data with configurable cloud virtual machines

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 101165
Author(s):  
Natalia N. Orlova ◽  
Olga V. Bogatova ◽  
Alexey V. Orlov
Keyword(s):  
High Performance ◽  
Virtual Machines

scholarly journals Ballooning Graphics Memory Space in Full GPU Virtualization Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Younghun Park ◽  
Minwoo Gu ◽  
Sungyong Park
Keyword(s):  
High Performance ◽  
Virtual Machines ◽  
Graphics Processing Unit ◽  
Performance Degradation ◽  
Processing Unit ◽  
Memory Space ◽  
Memory Size ◽  
Memory Sharing ◽  
Gpu Virtualization ◽  
Graphics Processing

Advances in virtualization technology have enabled multiple virtual machines (VMs) to share resources in a physical machine (PM). With the widespread use of graphics-intensive applications, such as two-dimensional (2D) or 3D rendering, many graphics processing unit (GPU) virtualization solutions have been proposed to provide high-performance GPU services in a virtualized environment. Although elasticity is one of the major benefits in this environment, the allocation of GPU memory is still static in the sense that after the GPU memory is allocated to a VM, it is not possible to change the memory size at runtime. This causes underutilization of GPU memory or performance degradation of a GPU application due to the lack of GPU memory when an application requires a large amount of GPU memory. In this paper, we propose a GPU memory ballooning solution called gBalloon that dynamically adjusts the GPU memory size at runtime according to the GPU memory requirement of each VM and the GPU memory sharing overhead. The gBalloon extends the GPU memory size of a VM by detecting performance degradation due to the lack of GPU memory. The gBalloon also reduces the GPU memory size when the overcommitted or underutilized GPU memory of a VM creates additional overhead for the GPU context switch or the CPU load due to GPU memory sharing among the VMs. We implemented the gBalloon by modifying the gVirt, a full GPU virtualization solution for Intel’s integrated GPUs. Benchmarking results show that the gBalloon dynamically adjusts the GPU memory size at runtime, which improves the performance by up to 8% against the gVirt with 384 MB of high global graphics memory and 32% against the gVirt with 1024 MB of high global graphics memory.

scholarly journals Viability of Cloud Computing for Real-Time Numerical Weather Prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1985-1996 ◽  
Author(s):  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
Roland Stull
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
High Performance ◽  
Virtual Machines ◽  
Weather Prediction ◽  
Cloud Platform ◽  
Numerical Weather ◽  
Performance Computing

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

Towards Using Cloud Elasticity on the Internet of Things Landscape

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Rodrigo Da Rosa Righi ◽  
Márcio Miguel Gomes ◽  
Cristiano Andrá Da Costa ◽  
Helge Parzyjegla ◽  
Hans-Ulrich Heiss
Keyword(s):  
Internet Of Things ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Virtual Machines ◽  
The Internet ◽  
Data Volume ◽  
Significant Performance ◽  
Cloud Elasticity ◽  
Performance Gains ◽  
The Internet Of Things

The digital universe is growing at significant rates in recent years. One of the main responsible for this sentence is the Internet of Things, or IoT, which requires a middleware that should be capable to handle this increase of data volume at real-time. Particularly, data can arrive in the middleware in parallel as in terms of input data from Radio-Frequency Identification (RFID) readers as request-reply query operations from the users side. Solutions modeled at software, hardware and/or architecture levels present limitations to handle such load, facing the problem of scalability in the IoT scope. In this context, this arti- cle presents a model denoted Eliot - Elasticity-driven Internet of Things - which combines both cloud and high performance computing to address the IoT scal- ability problem in a novel EPCglobal-compliant architecture. Particularly, we keep the same API but offer an elastic EPCIS component in the cloud, which is designed as a collection of virtual machines (VMs) that are allocated and deallocated on-the-fly in accordance with the system load. Based on the Eliot model, we developed a prototype that could run over any black-box EPCglobal- compliant middleware. We selected the Fosstrak for this role, which is currently one of the most used IoT middlewares. Thus, the prototype acts as an upper layer over the Fosstrak to offer a better throughput and latency performances in an effortless way. The results are encouraging, presenting significant performance gains in terms of response time and request throughput when comparing both elastic (Eliot) and non-elastic (standard Fosstrak) executions.  

A Hybrid Storage Access Framework for High-Performance Virtual Machines

2014 ◽  
Vol 13 (5s) ◽  
pp. 1-24 ◽  
Author(s):  
Chih-Kai Kang ◽  
Yu-Jhang Cai ◽  
Chin-Hsien Wu ◽  
Pi-Cheng Hsiu
Keyword(s):  
High Performance ◽  
Virtual Machines ◽  
Hybrid Storage

scholarly journals The Cost of being Object-Oriented: A Preliminary Study

1999 ◽  
Vol 7 (2) ◽  
pp. 87-95 ◽  
Author(s):  
Zoran Budimlić ◽  
Ken Kennedy ◽  
Jeff Piper
Keyword(s):  
High Performance ◽  
Virtual Machines ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Java Programming ◽  
Direct Translation ◽  
Full Power ◽  
Performance Penalty ◽  
The Cost ◽  
Good Object

Since the introduction of the Java programming language, there has been widespread interest in the use Java for the high performance scientific computing. One major impediment to such use is the performance penalty paid relative to Fortran. To support our research on overcoming this penalty through compiler technology, we have developed a benchmark suite, called OwlPack, which is based on the popular LINPACK library. Although there are existing implementations of LINPACK in Java, most of these are produced by direct translation from Fortran. As such they do not reflect the style of programming that a good object‐oriented programmer would use in Java. Our goal is to investigate how to make object‐oriented scientific programming practical. Therefore we developed two object‐oriented versions of LINPACK in Java, a true polymorphic version and a “Lite” version designed for higher performance. We used these libraries to perform a detailed performance analysis using several leading Java compilers and virtual machines, comparing the performance of the object‐oriented versions of the benchmark with a version produced by direct translation from Fortran. Although Java implementations have been made great strides, they still fall short on programs that use the full power of Java’s object‐oriented features. Our ultimate goal is to drive research on compiler technology that will reward, rather than penalize good object‐oriented programming practice.

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