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Five Ways Scale-Up Systems Save Money and Improve TCO

The move away from the traditional single processor/memory design has fostered new programming paradigms that address multiple processors (cores). Existing single core applications need to be modified to use extra processors (and accelerators). Unfortunately there is no single portable and efficient programming solution that addresses both scale-up and scale-out systems.

In Memory Data Grids

This white paper provides an overview of in-memory computing technology with a focus on in-memory data grids. It discusses the advantages and uses of in-memory data grids and introduces the GridGain In-Memory Data Fabric. Download this guide to learn more.

Scaling Software for In-Memory Computing

“The move away from the traditional single processor/memory design has fostered new programming paradigms that address multiple processors (cores). Existing single core applications need to be modified to use extra processors (and accelerators). Unfortunately there is no single portable and efficient programming solution that addresses both scale-up and scale-out systems.”

Scaling Hardware for In-Memory Computing

The two methods of scaling processors are based on the method used to scale the memory architecture and are called scaling-out or scale-up. Beyond the basic processor/memory architecture, accelerators and parallel file systems are also used to provide scalable performance. “High performance scale-up designs for scaling hardware require that programs have concurrent sections that can be distributed over multiple processors. Unlike the distributed memory systems described below, there is no need to copy data from system to system because all the memory is globally usable by all processors.”

In-Memory Computing for HPC

To achieve high performance, modern computer systems rely on two basic methodologies to scale resources: scale-up or scale-out. The scale-up in-memory system provides a much better total cost of ownership and can provide value in a variety of ways. “If the application program has concurrent sections then it can be executed in a “parallel” fashion. Much like using multiple bricklayers to build a brick wall. It is important to remember that the amount and efficiency of the concurrent portions of a program determine how much faster it can run on multiple processors. Not all applications are good candidates for parallel execution.”

Radio Free HPC Year End Review of 2016 Predictions

In this podcast, the Radio Free HPC team looks at how Shahin Khan fared with his OrionX 2016 Technology Issues and Predictions. “Here at OrionX.net, we are fortunate to work with tech leaders across several industries and geographies, serving markets in Mobile, Social, Cloud, and Big Data (including Analytics, Cognitive Computing, IoT, Machine Learning, Semantic Web, etc.), and focused on pretty much every part of the “stack”, from chips to apps and everything in between. Doing this for several years has given us a privileged perspective. We spent some time to discuss what we are seeing and to capture some of the trends in this blog.”

SGI UV 300RL Enables Real-time Analytics with Oracle Database In-Memory

Today SGI introduced the SGI UV 300RL for big data in-memory analytics. As a new model in the SGI UV server line certified and supported with Oracle Linux, the SGI UV 300RL provides up to 32 sockets and 24 terabytes of shared memory. The solution enables enterprises that have standardized on Intel-based servers to run Oracle Database In-Memory on a single system to help achieve real-time operations and accelerate data analytics at unprecedented scale.