#Daa decompressor software#

We contrast these approaches when possible, highlight their pros and cons, and suggest future research directions. The paper covers a broad spectrum of such approaches, outlining the basic philosophy of each approach and providing examples. Since video compression is multi-step in nature using various algorithms, parallel processing can be exploited at an individual algorithm or at a complete system level.

The paper provides a discussion of the basic compression techniques, existing video coding standards, and various parallelization approaches. This paper presents an overview of the recent research in video compression using parallel processing. Despite the availability of fast single processors, parallel processing helps to explore advanced algorithms and to build more sophisticated systems.

#Daa decompressor software#

For circumvent the computational requirement, researchers has resorted to parallel processing with a variety of approaches using dedicated parallel VLSI architectures as well as software on general-purpose available multiprocessor systems. Designing efficient coding algorithms continues to be a prolific area of research. Despite the tremendous progress in this area, video compression remains a challenging research problem due to its computational requirements and also because of the need for higher picture quality at lower data rates. Taking advantage of spatial, temporal, and statistical redundancies in video data, a video compression system aims to maximize the compression ratio while maintaining a high picture quality. Dryad, Giraph, Hoya, Hadoop MapReduce, REEF, Spark, Storm, Tez.ĭriven by the rapidly increasing demand for audio-visual applications, digital video compression technology has become a mature field, offering several available products based on both hardware and software implementations. We provide experimental evidence demonstrating the improvements we made, confirm improved efficiency by reporting the experience of running YARN on production environments (including 100% of Yahoo! grids), and confirm the flexibility claims by discussing the porting of several programming frameworks onto YARN viz. The new architecture we introduced decouples the programming model from the resource management infrastructure, and delegates many scheduling functions (e.g., task fault-tolerance) to per-application components. In this paper, we summarize the design, development, and current state of deployment of the next generation of Hadoop's compute platform: YARN. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler. For increasingly diverse companies, Hadoop has become the data and computational agorá-the de facto place where data and computational resources are shared and accessed. The initial design of Apache Hadoop was tightly focused on running massive, MapReduce jobs to process a web crawl. Our experimental results using five different MapReduce benchmarks show an average improvement of approximately 80% compared to standard Hadoop. The second P-Codec module is a decompressed map task divider that increases parallelism by dynamically changing the map task split sizes based on the size of the final decompressed block. This process reduces the runtime of a job by removing the burden of decompression during the MapReduce phase.

P-Codec includes two modules the first module decompresses data upon retrieval by a data node during the phase of uploading the data to the Hadoop Distributed File System (HDFS). In this paper, we propose the design and implementation of a Parallel Compressed File Decompressor (P-Codec) that improves the performance of Hadoop when processing compressed data. Consequently, standard Hadoop takes longer times to process. The number of map tasks created is determined by the size of the file, so by compressing a large file, the number of mappers is reduced which in turn decreases parallelism. However, this approach defeats one of Hadoop’s main purposes, which is the parallelism of map and reduce tasks. Data compression not only makes network I/O processing faster, but also provides better utilization of resources. One of the effective methods for handling big data sizes is compression. The increasing amount and size of data being handled by data analytic applications running on Hadoop has created a need for faster data processing.