oneAPI Implementations

oneAPI delivers a common developer experience across diverse architectures (CPU, GPU, FPGA and accelerators) for faster application performance, more productivity, and greater innovation.

Accelerating Healthcare Diagnostics with Intel oneAPI and AI Tools

Using AI to speed diagnostics for individuals or larger populations involves complex computing models that benefit from accelerated computing systems built on a mix of CPUs, GPUs, and other specialty processors. Over the past two years since the onset of COVID-19, several healthcare organizations and technology providers have found that Intel oneAPI and AI tools efficiently accelerate applications requiring high-performance, multiarchitecture computing.


Accelerate Scientific Rendering and reduce Data Storage constraints.

“Our ability to exploit cutting edge AI and machine learning technologies allows us to readily meet the fast-growing demands of big data and scientific visualization applications.” Kwan-Liu Ma (PI) – Professor, Computer Science Director, UC Davis Center for Visualization Head, VIDI Labs Faculty of GGCS, GGE, and ECEGP.


Dive achieves 1.5X speedup in Vectorization with latest Intel CPU and oneAPI Software – founded on the unified, open, standards-based oneAPI programming model.

Dive Optimized Its Fluid Solver for 4th Gen Intel® Xeon® Scalable Processors with Intel® AVX-512 using Intel® VTune™ Profiler


Chasing Exascale

TACC’s Frontera Supercomputer uses oneAPI multiarchitecture programming to accelerate scientific advancements.

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Argonne National Lab – Porting Simulation, Data-Intensive, and AI Applications to the Aurora Exascale System

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DP, Alibaba Cloud, and Intel: A Winning Solution to Maximize Computation Performance

DP needed to run LAMMPS workloads, which are particularly challenging due to their inherent complex simulations and changing dynamics. Using the combination of Alibaba’s E-HPC Cloud Service and Intel® hardware and oneAPI software, DP Technology achieved about 45.2% performance improvement.

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Codeplay’s Contribution to oneAPI by supporting SYCL, oneDNN and oneMKL for NVIDIA GPUs

Enabling software developers to write code once, and then tune it for multiple accelerator platforms, is the holy grail for the high-performance computing (HPC) and supercomputing industry. Such a breakthrough would eliminate writing separate code for multiple accelerator platforms, a time-consuming and costly exercise that takes talented developers away from working on critical projects.

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Fujitsu and RIKEN Optimized oneDNN for Improved Performance on ARM

Claiming the title of world’s fastest performance for the number of deep learning models trained per time unit for CosmoFlow, Fujitsu and RIKEN take first place for MLPerf HPC Benchmark with supercomputer Fugaku. By applying technology to programs used on the system that reduce the mutual interference of communication between CPUs, they were able to train the system at a rate of 1.29 deep learning models per minute – approximately 1.77 times faster than other systems.

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SYCL Helping to Accelerate Simulation for CERN Researchers

At the Supercomputing SC21 conference last year there were many research papers and presentations to help us learn more about how SYCL is being used across the industry. One of the sessions that stood out for me was presented by Dr. Vincent Pascuzzi from Brookhaven National Laboratory whose research paper was co-authored with Mehdi Goli, VP Research at Codeplay® and outlined the collaboration that is being done to advance research using the Large Hadron Collider.

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The oneAPI Specification joins the UXL Foundation. For more info:

Press Release