Towards distributed acceleration of image processing applications using reconfigurable active SSD clusters: a case study of seismic data analysis. (2018)

• Record Type: Journal Article
• Authors: Sharafeddin, Mageda; Partamian, Hmayag; Awad, Mariette; Saghir, Mazen A.R.; Akkary, Haitham; Artail, Hassan; Hajj, Hazem; Baydoun, Mohammed
• Is Part Of: International journal of high performance computing and networking. Volume 11:Number 4(2018)
• Page Start: 261
• Page End: 278
• Publication Date: 2018
• Subjects: active solid state devices -- data analysis -- distributed systems -- field programmable gate array -- FPGA -- grey level cooccurrence matrix -- GLCM -- Haralick attributes -- image processing applications -- intelligent systems -- machine learning -- parallel architectures -- reconfigurable computing -- seismic data analysis; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

In this work, we propose a high performance distributed system that consists of several middleware servers each connected to a number of FPGAs with extended solid state storage that we call reconfigurable active solid state device (RASSD) nodes. A full data communication solution between middleware and RASSD nodes is presented. We use seismic data analysis as a case study to quantify how and by how much RASSD nodes can accelerate computational throughput. Speedup of seismic data prediction time when both GLCM and Haralick features are accelerated is examined. The distributed system achieves 102× speedup compared to 4-thread openMP implementation and 265× speedup compared to single thread modern CPU performance. Performance is 5× better than previous work reporting speedup on GLCM and Haralick feature analysis when data is local to the FPGA and 20× better than an identical CUDA implementation using modern GPU.