In a recent survey, International Data Corporation (IDC) found that accelerated computing is quickly gaining traction in the enterprise as businesses embrace these technologies to overcome the limitations of CPUs. To help organizations to better understand where accelerated computing fits in the computing platforms hierarchy and to develop a more informed implementation strategy, IDC has published its first accelerated compute taxonomy.
Accelerated computing is the ability to accelerate applications and workloads by offloading a portion of the processing onto adjacent silicon subsystems such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs). Accelerated computing is gaining traction in the enterprise as businesses seek solutions for overcoming the limitations of central processing units (CPUs) for workloads that require data processing acceleration. Such apps/workloads rely on (single stream) pipeline processing where data locality is important for quick handoffs between various components.
Accelerated computing is used for unstructured data management workloads, including cognitive, deep learning, artificial intelligence, machine learning, and similar types of applications; data analytics workloads, including visual analytics; technical and scientific workloads; cloud computing, both internally and in the form of acceleration as a service; and edge computing. In addition, accelerated computing will affect most workloads as defined by IDC.
“Compute will become a lot less homogeneous as today’s acceleration technologies, like GPUs and FPGAs, and yet-to-be-developed accelerators start transforming server infrastructure to meet the performance demands of modern workloads, including cognitive and AI,” said Peter Rutten, research manager, Servers and Computing Platforms at IDC.
When asked which specific infrastructure attributes are important for deploying mission-critical workloads in their organization, nearly three quarters of the survey respondents identified single or multiple GPUs. GPUs are especially attractive to businesses as they can be procured off the shelf and utilize standard libraries that can be incorporated into applications easily. However, other technologies that offer potentially higher performance per watt such as FPGAs, many-core processors, and application specific integrated circuits (ASICs) are starting to gain traction as well.
“There’s a new world of possibilities with accelerators, which all have unique technical characteristics and capabilities allowing the right accelerator to be matched with the right workload,” said Gregoire Robinson, research analyst with IDC’s Servers and Computing Platforms team.
The new taxonomy, IDC’s Worldwide Accelerated Compute Taxonomy, 2017 (Doc #US42878517), provides an overview of key enterprise-focused accelerated computing hardware definitions. These definitions delineate the scope of IDC’s accelerated compute research, which is tightly connected to IDC’s servers and compute platforms research as well as IDC’s 3rd Platform coverage. IDC sees an increasing emphasis within enterprises on big data processing, data analytics, and cognitive/artificial intelligence (AI) applications that are challenging the capabilities of general-purpose processors in servers deployed in the datacenter, in the cloud, and at the edge.
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