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Independent consultant James Reinders has just published a comprehensive article on the NextPlatform.com Web site titled “CERN uses [Intel®] DL Boost, oneAPI to juice inference without accuracy loss,” which describes the use of deep learning and Intel® oneAPI by CERN to accelerate Monte Carlo simulations for Large Hadron Collider (LHC) research. Reinders writes that CERN researchers “have demonstrated success in accelerating inferencing nearly two-fold by using reduced precision without compromising accuracy at all.” The work is being carried out as part of Intel’s long-standing collaboration with CERN through CERN openlab.
If Reinders’ name looks familiar to you, that’s because he recently published a book about the use of Data Parallel C++ (DPC++), which is the foundation compiler technology at the heart of Intel oneAPI. (See “Springer and Intel publish new book on DPC++ parallel programming, and you can get a free PDF copy!”)
CERN researchers found that about half of the computations in a specific neural network (NN) called a Generative Adversarial Network (GAN) could be switched from FP32 to INT8 numerical precision, which is directly supported by Intel® DL Boost, without loss of accuracy. GAN performance doubled as a result while accuracy was not affected. Although this work was done using Intel® Xeon® Scalable Processors with direct INT8 support, Reinders’ article also makes the next logical jump:
“INT8 has broad support thanks to Intel Xeon [Scalable Processors], and it is also supported in Intel® Xe GPUs. FPGAs can certainly support INT8 and other reduced precision formats.”
Further, writes Reinders:
“The secret sauce underlying this work and making it even better: oneAPI makes Intel DL Boost and other acceleration easily available without locking in applications to a single vendor or device”
“It is worth mentioning how oneAPI adds value to this type of work. Key parts of the tools used, including the acceleration tucked inside TensorFlow and Python, utilize libraries with oneAPI support. That means they are openly ready for heterogeneous systems instead of being specific to only one vendor or one product (e.g. GPU).
“oneAPI is a cross-industry, open, standards-based unified programming model that delivers a common developer experience across accelerator architectures. Intel helped create oneAPI, and supports it with a range of open source compilers, libraries, and other tools. By programming to use INT8 via oneAPI, the kind of work done at CERN described in this article could be carried out using Intel Xe GPUs, FPGAs, or any other device supporting INT8 or other numerical formats for which they may quantize.”
For additional information about Intel oneAPI, see “Release beta09 of Intel® oneAPI Products Now Live – with new programming tools for FPGA acceleration including Intel® VTune™ Profiler.” You may also be interested in an instructor-led class titled “Using Intel® oneAPI Toolkits with FPGAs (IONEAPI).”
Notices & Disclaimers
Performance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.
Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software or service activation.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
If Reinders’ name looks familiar to you, that’s because he recently published a book about the use of Data Parallel C++ (DPC++), which is the foundation compiler technology at the heart of Intel oneAPI. (See “Springer and Intel publish new book on DPC++ parallel programming, and you can get a free PDF copy!”)
CERN researchers found that about half of the computations in a specific neural network (NN) called a Generative Adversarial Network (GAN) could be switched from FP32 to INT8 numerical precision, which is directly supported by Intel® DL Boost, without loss of accuracy. GAN performance doubled as a result while accuracy was not affected. Although this work was done using Intel® Xeon® Scalable Processors with direct INT8 support, Reinders’ article also makes the next logical jump:
“INT8 has broad support thanks to Intel Xeon [Scalable Processors], and it is also supported in Intel® Xe GPUs. FPGAs can certainly support INT8 and other reduced precision formats.”
Further, writes Reinders:
“The secret sauce underlying this work and making it even better: oneAPI makes Intel DL Boost and other acceleration easily available without locking in applications to a single vendor or device”
“It is worth mentioning how oneAPI adds value to this type of work. Key parts of the tools used, including the acceleration tucked inside TensorFlow and Python, utilize libraries with oneAPI support. That means they are openly ready for heterogeneous systems instead of being specific to only one vendor or one product (e.g. GPU).
“oneAPI is a cross-industry, open, standards-based unified programming model that delivers a common developer experience across accelerator architectures. Intel helped create oneAPI, and supports it with a range of open source compilers, libraries, and other tools. By programming to use INT8 via oneAPI, the kind of work done at CERN described in this article could be carried out using Intel Xe GPUs, FPGAs, or any other device supporting INT8 or other numerical formats for which they may quantize.”
For additional information about Intel oneAPI, see “Release beta09 of Intel® oneAPI Products Now Live – with new programming tools for FPGA acceleration including Intel® VTune™ Profiler.” You may also be interested in an instructor-led class titled “Using Intel® oneAPI Toolkits with FPGAs (IONEAPI).”
Notices & Disclaimers
Performance varies by use, configuration, and other factors. Learn more at www.Intel.com/PerformanceIndex.
Intel does not control or audit third-party data. You should consult other sources to evaluate accuracy.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. See backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software or service activation.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.
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