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Life Sciences are in the midst of a dramatic transformation as technology redefines what is possible for life as we know it. Discoveries will not wait – why should you?
Intel is enabling insights in molecular imaging, ranging from resolving protein structures, visualization of cell function, and analysis of molecular processes in living organisms. With these insights, life sciences are seeing applications in cancer, immunology, neurodegenerative disorders, and more.
Intel® Scalable Systems Framework delivers high performance, balanced, power-efficient, and reliable systems capable of supporting a wide range of compute-intensive and data-intensive life sciences analytics workloads, including genomics, molecular dynamics, molecular imaging, deep learning, and visualization.
Pre-validated SSF Integrated Solutions reduce time to insight (rapid prototyping, discovery, and data analytics) through improved configurability to match a given workload. SSF enables greater configurability and flexibility than contemporary supercomputers across new levels of memory, in the switching fabric, the I/O and storage subsystems, and within the interconnect. Intelligence at the hardware and software levels enables supercomputing sites to tune for optimal performance.
Intel® Xeon® Processors and Intel® Xeon Phi™ Processors have demonstrated significant performance gains across molecular dynamics, genomics, molecular imaging and machine learning. Intel offers a collection of benchmark and replication recipes, and optimized codes. When used, the optimized code can help researchers decipher their data and accelerate the path to discovery.
See how to take advantage of the Intel® Xeon Phi™ Product Family, a foundational element of Intel® Scalable System Framework (Intel® SSF), delivering competitive performance for deeper insights into your most complex data challenges.
Intel® Parallel Computing Centers include universities, institutions, and labs that are leaders in their fields. They focus on modernizing applications to increase parallelism and scalability through optimizations that leverage cores, caches, threads, and vector capabilities of microprocessors and coprocessors.
The Intel® Data Analytics Acceleration Library (Intel® DAAL) is designed to help software developers reduce the time it takes to develop their applications and provide them with improved performance. Intel® DAAL helps applications make better predictions faster and analyze larger data sets with the available compute resources at hand.
Accelerates math processing and neural network routines that increase application performance and reduce development time. Intel® MKL includes highly vectorized and threaded Linear Algebra, Fast Fourier Transforms (FFT), Neural Network, Vector Math, and Statistics functions. The easiest way to take advantage of all of that processing power is to use a carefully optimized math library.
Use the Intel® Distribution for Python* as a drop-in replacement for your current Python* environment to get high performance out of the box. Your Python* applications immediately gain significant performance and can further be tuned to extract every last bit of performance using the Intel® VTune™ Amplifier.
Intel offers a complete package to help computational biologists and scientific computing specialists determine how best to optimize a long-running workflow. Easy to use even on custom pipelines, the Workflow Profiler helps automate data collection and chart generation, and zero in on the key stages and areas of bottlenecks in the system.
Simplify the deployment and testing of cluster systems. Combining this standards-based cluster architecture with Intel® Xeon® processors and Intel® Xeon Phi™ processors provides a practical, cost-effective way to achieve dramatic performance gains for highly parallel applications, while simplifying software development and cluster maintenance.
Intel® Threading Building Blocks (Intel® TBB) 4.3 is a widely used, award-winning C and C++ Library for creating high-performance, scalable parallel applications. Enhance productivity and reliability with this rich set of components to efficiently implement higher-level, task-based parallelism, while tapping into multi-core and manycore processing power.