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In Memory Computing Speeds Results

In-Memory Computing can accelerate traditional applications by using a memory first design. Applicable to a wide range of domains, In-Memory Computing and In-Memory Data Grids take advantage of the latest trends in computer systems technology. “In-memory computing is designed to address some of the most critical and real-time task requirements today. This include real-time fraud detection, biometrics and border security and financial risk analytics. All of these use cases require very low latency access to data from very large amounts of data, which results in faster and more accurate decisions.”

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.

Speeding Workloads at the Dell EMC HPC Innovation Lab

The Dell EMC HPC Innovation Lab, substantially powered by Intel, has been established to provide customers best practices for configuring and tuning systems and their applications for optimal performance and efficiency through blogs, whitepapers and other resources. “Dell is utilizing the lab’s world-class Infrastructure to characterize performance behavior and to test and validate upcoming technologies.”

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.”

Selecting HPC Network Technology

“With three primary network technology options widely available, each with advantages and disadvantages in specific workload scenarios, the choice of solution partner that can deliver the full range of choices together with the expertise and support to match technology solution to business requirement becomes paramount.”

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.”

HPC Networking Trends in the TOP500

The TOP500 list is a very good proxy for how different interconnect technologies are being adopted for the most demanding workloads, which is a useful leading indicator for enterprise adoption. The essential takeaway is that the world’s leading and most esoteric systems are currently dominated by vendor specific technologies. The Open Fabrics Alliance (OFA) will be increasingly important in the coming years as a forum to bring together the leading high performance interconnect vendors and technologies to deliver a unified, cross-platform, transport-independent software stack.

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.”

Artificial Intelligence Becomes More Accessible

With the advent of heterogeneous computing systems that combine both main CPUs and connected processors that can ingest and process tremendous amounts of data and run complex algorithms, artificial intelligence (AI) technologies are beginning to take hold in a variety of industries. Massive datasets can now be used to drive innovation in industries such as autonomous driving systems, controlling power grids and combining data to arrive at a profitable decision, for example. Read how AI can now be used in various industries using the latest hardware and software.

High Performance System Interconnect Technology

Today, high performance interconnects can be divided into three categories: Ethernet, InfiniBand, and vendor specific interconnects. Ethernet is established as the dominant low level interconnect standard for mainstream commercial computing requirements. InfiniBand originated in 1999 to specifically address workload requirements that were not adequately addressed by Ethernet, and vendor specific technologies frequently have a time to market (and therefore performance) advantage over standardized offerings.