How DL processes calculate data in this way was also taken into consideration when selecting the development process that was ultimately used. It is being developed using an open development style that allows anyone to submit a pull request for source code they want to improve (request to incorporate improved source code into oneDNN). Supercomputer Fugaku is the world's highest level supercomputer, and is able to handle a wide range of application software with a high degree of execution performance. There was no DL process library for the Armv8-A instruction set, so we needed to develop a new one. We actually have tried using Translator to port parts of oneDNN v1.6 implemented with Xbyak to Armv8-A. The A64FX offers both, with 48 calculation cores, high memory bandwidth, 512bit wide SIMD and other cutting edge technology, resulting in excellent calculation performance in real-world applications where strong performance in both areas is needed. It would not be possible to quickly learn what information was encoded, and where. << /Filter /FlateDecode /Length 4661 >> I worked late into the night to make sure my answer was thorough. Translator generates machine code for Armv8-A instructions as follows. New DL processes are added daily, and Xbyak continues to be used to further optimize it for the x64 instruction set. When a user wants to run an application that uses a DL process, they use an API provided by the framework to define the neural network for the process to run and to describe processing details. oneDNN is the de facto standard for DL process libraries using CPUs, and it is already supported by a range of frameworks. The Next Platform is published by Stackhouse Publishing Inc in partnership with the UK’s top technology publication, The Register. hބSMo�0�+���(� )�C U 6%��`3��K����z߼�#��b�bSdR�۸�V�IQ���hQ�]Z����JY5�@I��NQ����v����5i�v�W���0̘��)����PvuA��pY*T*��.��g*vy�̡9�~_^��VœF �Y����f�-����C0�λ���������(���:�@��ݥt�l:b�+�� �3����#q}5y�d���Pw51��>4��*=3L�/�IzE�hb�B�.����l!�hK\�q�ʹ�$.�)&��飪��3��g�0F�����v����j�o����j�1m�a�5Bo�-�Σ�)�b��b7}M,��0�Y��b�q�=�W? On the other hand, developers who could code while understanding the implementation at the assembler level are few and far between these days, so it would be difficult to gather a team even if that option was a possibility. He has been involved in R&D of image codec LSIs and wireless sensor nodes, and is currently engaged in R&D of AI software for Arm HPC. That means there was some mistake made converting an instruction somewhere in a block of 10,000 steps. In about two weeks, we were able to get it running with sufficient processing speed1 on A64FX. Xbyak is used to generate x64 machine code. “The Fujitsu A64FX is the first processor available to us that actually implements these new SVE instructions. Since 2019, he leads R&D of AI software for Arm HPC as a manager. And then I woke up. oneDNN can be used to run a range of processes used in DL processing, such as convolution, batch_normalization, eltwise, pooling, and reorder. You would not even want to count that many. When a pull request has been submitted, the new source code is reviewed for bugs and tested to confirm that it improves the processing speed of oneDNN or helps to expand its functionality. When running on A64FX, the original oneDNN just compiled for Armv8-A is hundreds of times slower than that of optimized for Armv8-A as shown in the chart at the beginning. The new Fugaku supercomputer has been delivered to Port Island located off the coast of Kobe. We were surprised by the results even though we created it ourselves. We would then reverse assemble it and extract information from each x64 instruction in a way that made it easy to understand. Just by recompiling with the pre-release GCC 11.0 from Github the newer version returned closer to 800GB/s. k�׃ endstream endobj 2416 0 obj <>stream Copyright 2019-2020 FUJITSU LABORATORIES LTD. Executable code is generated during execution. In practice, he says, there’s some catching up for the compilers to do but it isn’t that far off. Fujitsu designed the A64FX for the Fugaku. “The Fujitsu A64FX is the first processor available to us that actually implements these new SVE instructions. In other words, we needed to implement and verify more than 4,000 functions to generate machine code. We’ve built our portfolio to help you achieve this with hybrid IT, end to end networking solutions and Digital Workplace Services. ViON's experience processing, storing, managing and protecting data in the Federal, State and Local, as well as Commercial markets allows us a unique capability to bring complex solutions to market,” said Tom Frana, Chairman and CEO of ViON. A64FX Features. I was able to keep up with the “spike fake-outs” by Arm and Intel, and ultimately had the source code merged. Seeing how the compiler efforts to date pan out to fully exploit the high performance and energy efficiency (see Fugaku’s status on the Top 500 and Green 500) is a task yet ahead, but so far, so good, Miller says. There are more than 4,000 types of instructions in the Armv8-A instruction set, if operand variations are also included. As I mentioned earlier, your Android smartphone or iPhone contains a CPU that uses the Armv8-A instruction set (however, it does not support SVE instructions). Here is how the performance stacks up comparing across the three generations of processors: If you would like to add functionality or try improving the source code, please do consider posting an ISSUE, submitting a pull request, or mail to arm_dl_oss[at mark] Luckily, I had Dr. Honda, who is an expert at optimization, sitting nearby for help. His GitHub account name is "kurihara-kk". Next, the Intel developer had invited a developer from Arm to join in, since my RFP concerned an Arm product. First, we would generate all the x64 machine code. The subroutines generated by oneDNN using Xbyak can be very complicated, with the most complicated subroutines consisting of instructions with more than 10,000 steps. Steps 1 through 3 are performed for all x64 machine code generated using Xbyak. Applications that make use of deep learning processes (hereinafter called “DL processes”) normally consists of a software stack formed from two layers: a framework layer and a library layer (as shown below). �"��8v008���Ux�{�ȟ�t��L�>�L�L� �!�Ć��L��~��z*��[��� In order to port oneDNN to Armv8-A, we needed to create new software that would implement the same functionality as Xbyak for the Armv8-A instruction set. Reliability is a must for large-scale parallel processing systems. Xbyak is used to implement a range of processes used by DL processing for oneDNN, such as convolution, batch_normalization, eltwise, pooling, and reorder. He has been involved in R&D of embedded multi-core processor software, HEVC codec LSIs, wireless sensor nodes and visualization of wireless communication interference, and is currently engaged in R&D of AI software for Arm HPC. In order to get the maximum performance out of A64FX, optimized JIT code should be assembled using Xbyak_aarch64 directly for handling bottlenecks instead of going through Translator.↩, fltechさんは、はてなブログを使っています。あなたもはてなブログをはじめてみませんか?, Powered by Hatena Blog A64FX boosts performance up by microarchitectural enhancements, 512-bit wide SIMD, HBM2 and process technology • > 2.5x faster in HPC/AI benchmarks than SPARC64 XIfx (Fujitsu’s previous HPC CPU) •The results are based on the Fujitsu compiler optimized for our microarchitecture and SVE Performance 0 2 4 6 8 DGEMM Stream Triad Fluid dynamics If you consider just the first feature, you might wonder how this is any different from an inline assembler or specifying assembler instructions using intrinsic functions. Xbyak offers the following features. Of course, it’s not just about taking the hardware for a test drive.