scholarly journals GPGPU Task Scheduling Technique for Reducing the Performance Deviation of Multiple GPGPU Tasks in RPC-Based GPU Virtualization Environments

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 508
Author(s):  
Jihun Kang ◽  
Heonchang Yu
Keyword(s):  
Task Scheduling ◽  
Virtual Machines ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Fair Use ◽  
Processing Unit ◽  
Multiple User ◽  
Long Time ◽  
Gpu Virtualization ◽  
Graphics Processing

In remote procedure call (RPC)-based graphic processing unit (GPU) virtualization environments, GPU tasks requested by multiple-user virtual machines (VMs) are delivered to the VM owning the GPU and are processed in a multi-process form. However, because the thread executing the computing on general GPUs cannot arbitrarily stop the task or trigger context switching, GPU monopoly may be prolonged owing to a long-running general-purpose computing on graphics processing unit (GPGPU) task. Furthermore, when scheduling tasks on the GPU, the time for which each user VM uses the GPU is not considered. Thus, in cloud environments that must provide fair use of computing resources, equal use of GPUs between each user VM cannot be guaranteed. We propose a GPGPU task scheduling scheme based on thread division processing that supports GPU use evenly by multiple VMs that process GPGPU tasks in an RPC-based GPU virtualization environment. Our method divides the threads of the GPGPU task into several groups and controls the execution time of each thread group to prevent a specific GPGPU task from a long time monopolizing the GPU. The efficiency of the proposed technique is verified through an experiment in an environment where multiple VMs simultaneously perform GPGPU tasks.

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.

Comprehensive regression-based model to predict performance of general-purpose graphics processing unit

2019 ◽  
Vol 23 (2) ◽  
pp. 1505-1516 ◽  
Author(s):  
Mohammad Hossein Shafiabadi ◽  
Hossein Pedram ◽  
Midia Reshadi ◽  
Akram Reza
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
Graphics Processing

AN EVALUATION OF MULTIPLE FEED-FORWARD NETWORKS ON GPUs

2011 ◽  
Vol 21 (01) ◽  
pp. 31-47 ◽  
Author(s):  
NOEL LOPES ◽  
BERNARDETE RIBEIRO
Keyword(s):  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Low Cost ◽  
Back Propagation ◽  
General Purpose ◽  
Training System ◽  
Graphics Hardware ◽  
Processing Unit ◽  
Data Parallel ◽  
Graphics Processing

The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.

scholarly journals GPU-accelerated alignment of bisulfite-treated short-read sequences

2017 ◽  
Author(s):  
Richard Wilton ◽  
Xin Li ◽  
Andrew P. Feinberg ◽  
Alexander S. Szalay
Keyword(s):  
Dna Sequences ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
Short Read ◽  
Wide Range ◽  
Programming Logic ◽  
Short Read Aligner ◽  
Graphics Processing ◽  
Better Than

AbstractThe alignment of bisulfite-treated DNA sequences (BS-seq reads) to a large genome involves a significant computational burden beyond that required to align non-bisulfite-treated reads. In the analysis of BS-seq data, this can present an important performance bottleneck that can potentially be addressed by appropriate software-engineering and algorithmic improvements. One strategy is to integrate this additional programming logic into the read-alignment implementation in a way that the software becomes amenable to optimizations that lead to both higher speed and greater sensitivity than can be achieved without this integration.We have evaluated this approach using Arioc, a short-read aligner that uses GPU (general-purpose graphics processing unit) hardware to accelerate computationally-expensive programming logic. We integrated the BS-seq computational logic into both GPU and CPU code throughout the Arioc implementation. We then carried out a read-by-read comparison of Arioc's reported alignments with the alignments reported by the most widely used BS-seq read aligners. With simulated reads, Arioc's accuracy is equal to or better than the other read aligners we evaluated. With human sequencing reads, Arioc's throughput is at least 10 times faster than existing BS-seq aligners across a wide range of sensitivity settings.The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license.

scholarly journals Analysis of Fast Fourier Transformations algorithm for CUDA Architecture

2012 ◽  
Vol 53 ◽  
Author(s):  
Beatričė Andziulienė ◽  
Evaldas Žulkas ◽  
Audrius Kuprinavičius
Keyword(s):  
Graphics Processing Unit ◽  
General Purpose ◽  
Fast Fourier Transformation ◽  
Processing Unit ◽  
Data Allocation ◽  
Analysis Method ◽  
Central Processing ◽  
Execution Speed ◽  
Cuda Architecture ◽  
Graphics Processing

In this work Fast Fourier transformation algorithm for general purpose graphics processing unit processing (GPGPU) is discussed. Algorithm structure and individual stages performance were analysed. With performance analysis method algorithm distribution and data allocation possibilities were determined, depending on algorithm stages execution speed and algorithm structure. Ratio between CPU and GPU execution during Fast Fourier transform signal processing was determined using computer-generated data with frequency. When adopting CPU code for CUDA execution, it not becomes more complex, even if stream procesor parallelization and data transfering algorith stages are considered. But central processing unit serial execution).

Sign in / Sign up

Export Citation Format

Share Document