390 research outputs found
A Compression Technique Exploiting References for Data Synchronization Services
Department of Computer Science and EngineeringIn a variety of network applications, there exists significant amount of shared data between two end hosts. Examples include data synchronization services that replicate data from one node to another. Given that shared data may have high correlation with new data to transmit, we question how such shared data can be best utilized to improve the efficiency of data transmission. To answer this, we develop an encoding technique, SyncCoding, that effectively replaces bit sequences of the data to be transmitted with the pointers to their matching bit sequences in the shared data so called references. By doing so, SyncCoding can reduce data traffic, speed up data transmission, and save energy consumption for transmission. Our evaluations of SyncCoding implemented in Linux show that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication. The gains of SyncCoding over those techniques in the perspective of data size after compression in a cloud storage scenario are about 12.4%, 20.1%, 29.9%, and 61.2%, and are about 78.3%, 79.6%, 86.1%, and 92.9% in a web browsing scenario, respectively.ope
CODAG: Characterizing and Optimizing Decompression Algorithms for GPUs
Data compression and decompression have become vital components of big-data
applications to manage the exponential growth in the amount of data collected
and stored. Furthermore, big-data applications have increasingly adopted GPUs
due to their high compute throughput and memory bandwidth. Prior works presume
that decompression is memory-bound and have dedicated most of the GPU's threads
to data movement and adopted complex software techniques to hide memory latency
for reading compressed data and writing uncompressed data. This paper shows
that these techniques lead to poor GPU resource utilization as most threads end
up waiting for the few decoding threads, exposing compute and synchronization
latencies.
Based on this observation, we propose CODAG, a novel and simple kernel
architecture for high throughput decompression on GPUs. CODAG eliminates the
use of specialized groups of threads, frees up compute resources to increase
the number of parallel decompression streams, and leverages the ample compute
activities and the GPU's hardware scheduler to tolerate synchronization,
compute, and memory latencies. Furthermore, CODAG provides a framework for
users to easily incorporate new decompression algorithms without being burdened
with implementing complex optimizations to hide memory latency. We validate our
proposed architecture with three different encoding techniques, RLE v1, RLE v2,
and Deflate, and a wide range of large datasets from different domains. We show
that CODAG provides 13.46x, 5.69x, and 1.18x speed up for RLE v1, RLE v2, and
Deflate, respectively, when compared to the state-of-the-art decompressors from
NVIDIA RAPIDS
A survey of parallel algorithms for fractal image compression
This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon
which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm
DCT Implementation on GPU
There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform
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