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.”
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.
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.”
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.
Applications such as machine learning and deep learning require incredible compute power, and these are becoming more crucial to daily life every day. These applications help provide artificial intelligence for self-driving cars, climate prediction, drugs that treat today’s worst diseases, plus other solutions to more of our world’s most important challenges. There is a multitude of ways to increase compute power but one of the easiest is to use the most powerful GPUs.
A survey conducted by insideHPC and Gabriel Consulting in Q4 of 2105 indicated that nearly 45% of HPC and large enterprise customers would spend more on system interconnects and I/O in 2016, with 40% maintaining spending at the same level as the prior year. For manufacturing, the largest subset representing approximately one third of the respondents, over 60% were planning to spend more and almost 30% maintaining the same level of spending going into 2016 implying the critical value of high performance interconnects.
Here’s a recap of SC16 announcements from Intel that are designed to provide even more powerful capabilities to address HPC challenges like energy efficiency, system complexity, and the ability for simplified workload customization. In supercomputing, one size certainly does not fit all. Intel’s new and updated technologies take a step forward in addressing these issues, allowing users to focus more on their applications for HPC, not the technology behind it.
Accelerated computing continues to gain momentum as the HPC community moves towards Exascale. Our recent Tesla P100 GPU review shows how these accelerators are opening up new worlds of performance vs. traditional CPU-based systems and even vs. NVIDIA’s previous K80 GPU product. We’ve got benchmarks, case studies, and more in the insideHPC Research Report on GPU Accelerators.
A workflow to support genomic sequencing requires a collaborative effort between many research groups and a process from initial sampling to final analysis. Learn the 4 steps involved in pre-processing.
In this research report, we reveal recent research showing that customers are feeling the need for speed—i.e. they’re looking for more processing cores. Not surprisingly, we found that they’re investing more money in accelerators like GPUs and moreover are seeing solid positive results from using GPUs. In the balance of this report, we take a look at these finding and and the newest GPU tech from NVIDIA and how it performs vs. traditional servers and earlier GPU products.