“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.”
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.
To achieve high performance, modern computer systems rely on two basic methodologies to scale resources. A scale-up design that allows multiple cores to share a large global pool of memory and a scale-out design design that distributes data sets across the memory on separate host systems in a computing cluster. To learn more about In-Memory computing download this guide from IHPC and SGI.
SGI’s Data Management Framework (DMF) software – when used within personalized medicine applications – provides a large-scale, storage virtualization and tiered data management platform specifically engineered to administer the billions of files and petabytes of structured and unstructured fixed content generated by highly scalable and extremely dynamic life sciences applications.
In life sciences, perhaps more than any other HPC discipline, simplicity is key. The SGI solution meets this requirement by delivering a single system that scales to huge capabilities by unifying compute, memory, and storage. Researchers and scientists in personalized medicine (and most life sciences) are typically not computer science experts and want a simple development and usage model that enables them to focus on their research and projects.
FPGAs will become increasing important for organizations that have a wide range of applications that can benefit from performance increases. Rather than a brute force method to increasing performance in a data center by purchasing and maintaining racks of hardware and associated costs, FPGAs may be able to equal and exceed the performance of additional servers, while reducing costs as well.
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.
From Megaflops to Gigaflops to Teraflops to Petaflops and soon to be Exaflops, the march in HPC is always on and moving ahead. This whitepaper details some of the technical challenges that will need to be addressed in the coming years in order to get to exascale computing.
With a massive surge in genomics research, the ability to quickly process very large amounts of data is now required for any organization that is involved in genomics. While the cost has been reduced significantly, the amount of data that is produced is has increased as well. This article describes next generation sequencing and how a combination of hardware and innovative software can decrease the amount of time to sequence genomes.
The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market — efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.