Getting the benefits of big data analytics can be challenging, but it is a necessary endeavor for any organization to succeed going forward. Understanding the challenges to maximizing big data analytics and DL, and how to overcome them, is crucial. Determining your expectations up front, and carefully orchestrating your infrastructure build, will allow you to construct an architecture […]
Silicon Mechanics Delivers 10x HPC Run Time Boost for Oklahoma Medical Research Foundation
[SPONSORED CONTENT] In biomedical research it’s accelerate or perish. Drug discovery is a trial-and-error process driven by simulations – faster simulations, enabled by compute- and data-intensive technologies, mean more runs in less time, resulting in errors identified and solutions achieved. Established in 1946, the Oklahoma Medical Research Foundation is a nonprofit research institute with more than 450 staff and over 50 labs studying cancer, heart disease, autoimmune disorders and aging-related diseases. OMRF discoveries led to the first, U.S.-approved therapy targeting sickle cell disease and the first approved treatment for neuromyelitis optica spectrum disorder, an autoimmune disease. The foundation’s research is enabled in part by advanced technology – accelerated clusters and high-performance data storage that support workloads fueled by massive data sets.
How You Can Use Artificial Intelligence in the Financial Services Industry
In financial services, it is important to gain any competitive advantage. Your competition has access to most of the same data you do, as historical data is available to everyone in your industry. Your advantage comes with the ability to exploit that data better, faster, and more accurately than your competitors. With a rapidly fluctuating market, the ability to process data faster gives you the opportunity to respond quicker than ever before. This is where AI-first intelligence can give you the leg
up.
Overcoming Challenges to Deep Learning Infrastructure
With use cases like computer vision, natural language processing, predictive modeling, and much more, deep learning (DL) provides the kinds of far-reaching applications that change the way technology can impact human existence. The possibilities are limitless, and we’ve just scratched the surface of its potential. There are three significant obstacles for you to be aware of when designing a deep learning infrastructure: scalability, customizing for each workload, and optimizing workload performance.
Successfully Deploy Composable Infrastructure on the Edge to Improve HPC and AI Outside of Traditional Data Centers
Emerging CDI technologies allow you to achieve the cost and availability benefits of cloud computing using on-premises networking equipment. You also benefit from extreme flexibility, being able to dynamically recompose systems and support nearly any workload. Thanks to innovative engineering, these benefits are now available on the edge.
Factors to Understand to Maximize Cloud HPC Investments for Your Organization
HPC workloads place considerable demands on compute hardware and IT infrastructures, which is why most organizations have traditionally kept HPC applications on-premises in their local data centers. Several cloud providers now offer specific HPC services and capabilities, but you’ll need to understand and address several factors before running HPC applications in the public cloud instead of locally in your own data center.
How Well-Designed Infrastructure Can Overcome Challenges to Big Data Analytics Workloads
In this sponsored post, our friends over at Silicon Mechanics discuss how using big data analytics and predictive analytics through deep learning (DL) are essential strategies to make smarter, more informed decisions and provide competitive advantages for your organization. But these tactics are not simple to execute, and they require a properly designed hardware infrastructure.
How Aerospace/Defense Can Harness Data with a Well-Designed AI Infrastructure
In this sponsored post, our friends over at Silicon Mechanics discuss how solving mission-critical problems using AI in the aerospace and defense industry is becoming more of a reality. Every day, new technologies emerge that can simplify deployment, management, and scaling of AI infrastructure to ensure long-term ROI. There are several questions to ask yourself to ensure deploying AI workloads, and harnessing the full potential of data, in aerospace/defense is much more plausible and efficient.
Improving AI Inference Performance with GPU Acceleration in Aerospace and Defense
The aerospace/defense industry often must solve mission-critical problems as they arise while also planning and designing for the rigors of future workloads. Technology advancements let aerospace/defense agencies gain the benefits of AI, but it’s essential to understand these advancements and the infrastructure requirements for AI training and inference.
Overcome Form Factor and Field Limitations with AI/HPC Workloads on the Edge
In this sponsored post, our friends over at Silicon Mechanics discuss how form factor, latency, and power can all be key limitations, but find out how key advancements in technology will allow higher performance at the edge. For this discussion, the edge means any compute workloads taking place outside of both cloud and traditional on-prem data centers.