Accelerate AI and HPC Workload Delivery with Composable Cloud Infrastructure

Join Intel, ScaleMatrix, and 451 Research for an insightful webinar covering the challenges and solutions to the adoption of Artificial Intelligence workloads: Deep Learning, Machine Learning and HPC, in the Cloud. High Performance Computing is a fundamental tool used in analytics and research; and on average, can provide an ROI as high as $515 per $1 of investment. However, HPC could deliver even greater value if we could overcome key technical barriers associated with different workloads driving separate systems with specialized hardware and software for each workload. These challenges lead to increased acquisition and management costs and are often responsible for reducing efficiency. Learn how ScaleMatrix’ high-density data centers and cloud delivery capabilities along with Intel’s Scalable Systems Framework have overcome these challenges with tight system integration, increased density, and balanced compute, memory, network/fabric and storage in a composable, cloud-delivered architecture, specifically designed to address the various AI and HPC requirements associated with compute intense workloads like Genomic Analytics, Molecular Dynamics, and Machine Learning. The ScaleMatrix Artificial Intelligence Cloud solution using Intel’s Scalable System Framework employs heterogeneous blocks of compute nodes that can be readjusted to match workload demand. It is easily scaled to meet any size customer need, offers ease of use, faster delivery times, and lower TCO by simplifying and improving core infrastructure. Technologies highlighted include the Intel® Xeon and Xeon Phi processors, Intel® Arria FPGA, Intel® SSD’s, and Intel® Omni-Path Architecture. Whether you’re focused on Image Processing, Convolutional Neural Networks, Alignment Algorithms, or DNA Sequencing, you’ll hear from Intel and ScaleMatrix experts how their joint solution can improve your HPC workload performance in an economical way. Complementary trial details will be presented.