高性能資料分析 (PHDA)的全球市場預測 (2016∼2020年)
Worldwide High-Performance Data Analysis Forecast, 2016-2020
|出版日期||內容資訊||英文 13 Pages
|高性能資料分析 (PHDA)的全球市場預測 (2016∼2020年) Worldwide High-Performance Data Analysis Forecast, 2016-2020|
|出版日期: 2016年06月20日||內容資訊: 英文 13 Pages||
數值建模和模擬隨著透過HPC (高性能運算) 的巨量資料分析·活用之發展而建立，一般認為這將會是長期性活用的可利用手法。具體而言包含大規模的圖表分析和意義分析技術，知識探索演算法，及這些新技術的組合等。在科學研究及各種產業等廣泛領域，可望一面利用公共/私有雲端，同時促進HPC和HPDA (高性能資料分析)的日益普及。
本報告提供全球高性能資料分析 (HPDA) 用伺服器市場──配合資料集中性 (巨量資料) 負載的HPC (高性能運算) 伺服器系統市場──之調查分析，提供您市場現狀與成長預測。
This IDC study centers on IDC's worldwide revenue forecast for HPDA servers, that is, HPC server systems that are acquired primarily to run data-intensive (Big Data) workloads. HPC Big Data activity may employ long-standing methods based on numerical modeling and simulation; newer methods such as large-scale graph analytics, semantic technologies, and knowledge discovery algorithms; or a combination of long-standing and newer methods.According to Steve Conway, IDC research vice president for High Performance Computing, "The goal of HPDA activity is typically to maximize insights and innovation by applying both established and newer methods to the same scientific, industrial, or commercial problem, often using the same HPC cluster. In a growing number of cases, however, buyers are acquiring HPC systems for dedicated use on mission-critical, high-performance analytics workloads. Buyers are also turning more often to public and hybrid cloud resources to tackle HPDA problems. IDC believes that the data explosion in science, engineering, and commercial analytics is bound to drive rapid growth in HPDA usage. Nearly all HPC industry/application segments, along with a growing number of commercial first-time HPC adopters, have potential use cases for HPDA. Finally, the application to machine and deep learning of HPC-style parallelism, data movement, and big memories promises to transform that formative market over time."