The Comparison of the Relative Entropy for Intrusion Detection on CPU and GPU

2012 24th Euromicro Conference on Real-Time Systems

2012

GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPG-PUs. Moreover, in our approach, a GPGPU job and memory copy between a GPGPU and the hosting CPU are run concurrently to enhance the responsiveness. To show the feasibility of our approach, we have implemented a prototype system for preemptive job executions and data copies in a GPGPU. The experimental results show that our approach can bound the response times in a reliable manner. In addition, the response time of our approach is significantly shorter than those of the unmodified GPGPU runtime system that supports no preemption and an advanced GPGPU model designed to support prioritization and performance isolation via preemptive data copies.

Section: Related Workmentioning

confidence: 99%

Supporting Preemptive Task Executions and Memory Copies in GPGPUs

Başaran

2012 24th Euromicro Conference on Real-Time Systems

2012

“…As GPU computational capacities of GPUs increase, GPUs are used to support diverse applications, such as sorting [25], text processing [6], Fast Fourier Transform [10], matrix operations [4], databases [1], encryption and decryption [24], intrusion detection [20], and biological applications [18]. These efforts call for a more general parallel programming framework, such as MapReduce, to exploit the parallel computational power provided by CPUs and GPUs with lower complexity compared with manual architectural optimizations.…”

Section: Multi-gpu Frameworkmentioning

confidence: 99%

Moim: A Multi-GPU MapReduce Framework

Xie

2013 IEEE 16th International Conference on Computational Science and Engineering

Başaran³

2013

MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce framework, called Moim, which 1) effectively utilizes both CPUs and GPUs (general purpose Graphics Processing Units), 2) overlaps CPU and GPU computations, 3) enhances load balancing in the map and reduce phases, and 4) efficiently handles not only fixed but also variable size data. We have implemented Moim and compared its performance with an advanced multi-GPU MapReduce framework. Moim achieves 20% − 90% speedup for different data sizes and numbers of the GPUs used for data processing.

“…There have been many recent applications of GPU computing to different problem domains such as Fast Fourier Transform [20], matrix operations [21], encryption and decryption [22], genetics [23], intrusion detection [24], and databases [25]. Using GPUs, these research efforts have achieved significant performance improvement [26,27].…”

Section: Related Workmentioning

confidence: 99%

Grex: An efficient MapReduce framework for graphics processing units

Başaran

Journal of Parallel and Distributed Computing

2013

In this paper, we present a new MapReduce framework, called Grex, designed to leverage general purpose graphics processing units (GPUs) for parallel data processing. Grex provides several new features. First, it supports a parallel split method to tokenize input data of variable sizes, such as words in e-books or URLs in web documents, in parallel using GPU threads. Second, Grex evenly distributes data to map/reduce tasks to avoid data partitioning skews. In addition, Grex provides a new memory management scheme to enhance the performance by exploiting the GPU memory hierarchy. Notably, all these capabilities are supported via careful system design without requiring any locks or atomic operations for thread synchronization. The experimental results show that our system is up to 12.4x and 4.1x faster than two state-of-the-art GPU-based MapReduce frameworks for the tested applications.