January 6, 2017
Bio-processor developer Edico Genome is collaborating with storage specialist Dell EMC to bundle computing and storage for analyzing gene-sequencing data. Th Read more…
November 20, 2012
NVIDIA, Intel and AMD were not the only chip vendors unveiling new HPC accelerators last week SC12. Texas Instruments (TI) announced a set of heterogeneous processors that they believe will offer among the best performance per watt in the industry. In this case, the chipmaker glued an ARM CPU and DSP together on the same die, offering a low-power SoC with an impressive number of FLOPS. Read more…
November 5, 2012
Chipmaker unveils Opteron 6300 series -- same core count, more performance. Read more…
September 28, 2012
Chipmaker Adapteva is attempting to bypass the conventional venture capital funding route and collect money via a micro-investor platform known as Kickstarter. In the process, the company will open up its software and hardware design for its manycore Epiphany architecture, and deliver a parallel computing kit to anyone who can ante up $99. Read more…
September 24, 2012
Chip manufacturer plans to offer 14nm FinFET transistors in 2014. Read more…
September 10, 2012
Intel has begun to formulate a strategy that will integrate fabric controllers with its server processors. According to Raj Hazra, general manager of the Technical Computing unit at Intel, the company is planning to use the recently acquired IP from Cray, QLogic and Fulcrum to deliver chips that put what is essentially a NIC onto the processor die. In a recent conversation with Hazra, he outlined their new fabric interconnect strategy. Read more…
August 22, 2012
Chipmaker Adapteva is sampling its 4th-generation multicore processor, known as Epiphany-IV. The 64-core chip delivers a peak performance of 100 gigaflops and draws just two watts of power, yielding a stunning 50 gigaflops/watt. The engineering samples were manufactured by GLOBALFOUNDRIES on its latest 28nm process technology. Read more…
August 16, 2012
In a recent report in Real World Technologies, chip guru David Kanter dissects the new 64-bit ARM design and what it might mean to the IT landscape. His take on the architecture is almost uniformly positive, noting that not only did the designers manage to develop an elegant instruction set that was backwardly compatible with the existing ISA, but they also took the extra step to jettison a few of the poorly designed features of the 32-bit architecture. Read more…
The increasing complexity of electric vehicles result in large and complex computational models for simulations that demand enormous compute resources. On-premises high-performance computing (HPC) clusters and computer-aided engineering (CAE) tools are commonly used but some limitations occur when the models are too big or when multiple iterations need to be done in a very short term, leading to a lack of available compute resources. In this hybrid approach, cloud computing offers a flexible and cost-effective alternative, allowing engineers to utilize the latest hardware and software on-demand. Ansys Gateway powered by AWS, a cloud-based simulation software platform, drives efficiencies in automotive engineering simulations. Complete Ansys simulation and CAE/CAD developments can be managed in the cloud with access to AWS’s latest hardware instances, providing significant runtime acceleration.
Two recent studies show how Ansys Gateway powered by AWS can balance run times and costs, making it a compelling solution for automotive development.
Five Recommendations to Optimize Data Pipelines
When building AI systems at scale, managing the flow of data can make or break a business. The various stages of the AI data pipeline pose unique challenges that can disrupt or misdirect the flow of data, ultimately impacting the effectiveness of AI storage and systems.
With so many applications and diverse requirements for data types, management systems, workloads, and compliance regulations, these challenges are only amplified. Without a clear, continuous flow of data throughout the AI data lifecycle, AI models can perform poorly or even dangerously.
To ensure your AI systems are optimized, follow these five essential steps to eliminate bottlenecks and maximize efficiency.
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