“The pharmaceutical industry trend toward joint ventures and collaborations has created a need for new platforms in which to work together. We’ll dive into architectural decisions for building collaborative systems. Examples include how such a platform allowed Human Longevity, Inc. to accelerate software deployment to production in a fast-paced research environment, and how Celgene uses AWS for research collaboration with outside universities and foundations.”
“The complexity and high costs of architecting and maintaining streaming analytics solutions often make it difficult to get new projects off the ground. That’s part of the reason Kx, a leading provider of high-volume, high-performance databases and real-time analytics solutions, is always interested in exploring how new technologies may help it push streaming analytics performance and efficiency boundaries. The Intel Xeon Phi processor is a case in point. At SC16 in Salt Lake City, Kx used a 1.2 billion record database of New York City taxi cab ride data to demonstrate what the Intel Xeon Phi processor could mean to distributed big data processing. And the potential cost/performance implications were quite promising.”
Researchers at the University of Wisconsin–Madison are developing new computer chips that combine tasks usually kept separate by design. According to assistant professor Jing Li, these “liquid silicon” chips can be configured to perform complex calculations and store massive amounts of information within the same integrated unit — and communicate efficiently with other chips. “There’s a huge bottleneck when classical computers need to move data between memory and processor,” says Li. “We’re building a unified hardware that can bridge the gap between computation and storage.”
“Adaptive Computing is driving up our customers’ productivity by helping them gain true insight into how their resources are being used, how to handle future capacity planning, and the service levels they are delivering to their most critical projects,” says Marty Smuin, CEO of Adaptive Computing. “This latest solution helps deliver the insights that organizations need in order to eliminate waste, avoid unnecessary delays, and make the changes that will align resources to better achieve organizational goals.”
“Real-time-analytics and Big Data environments are extremely demanding and the network is critical in linking together the extra high performance IBM POWER based servers and Tencent Cloud’s massive amounts of data,”said Amir Prescher, Sr. Vice President, Business Development, at Mellanox Technologies. “Tencent Cloud developed an optimized hardware/software platform to achieve new computing records, showing that Mellanox’s 100Gb/s Ethernet technology can deliver total infrastructure efficiency and improves application performance, making them ideal for Big Data applications.”
Today Atos announced Bull Director for HPSS, Data Management software dedicated to High Performance Computing. Bull Director for HPSS optimizes current large scale storage solutions and frees up compute time for users. “In a context of data explosion, storage is often a bottleneck and has a negative impact on application performance. Atos has a long experience of implementing HPSS in challenging environments where long-term data preservation and re-use of massive data sets are key. Our ultimate objective with Bull Director for HPSS and the other future components is to get rid of these bottlenecks and free up compute time for users.” explains Eric Eppe, Head of Products and Solutions for extreme computing at Atos.
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.
“Unchecked data growth and data sprawl are having a profound impact on life science workflows. As data volumes continue to grow, researchers and IT leaders face increasingly difficult decisions about how to manage this data yet keep the storage budget in check. Learn how these challenges can be overcome through active data management and leveraging cloud technology. The concepts will be applied to an example architecture that supports both genomic and bioimaging workflows.”
Today Fujitsu Laboratories announced a collaboration with the University of Toronto to develop a new computing architecture to tackle a range of real-world issues by solving combinatorial optimization problems that involve finding the best combination of elements out of an enormous set of element combinations. “This architecture employs conventional semiconductor technology with flexible circuit configurations to allow it to handle a broader range of problems than current quantum computing can manage. In addition, multiple computation circuits can be run in parallel to perform the optimization computations, enabling scalability in terms of problem size and processing speed.”