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Alexander Lyashevsky - XeHE: an Intel GPU Accelerated Fully Homomorphic Encryption Library: A SYCL Sparkler: Making the Most of C++ and SYCL

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Alexander Lyashevsky XeHE: an Intel GPU Accelerated Fully Homomorphic Encryption Library: A SYCL Sparkler: Making the Most of C++ and SYCL
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XeHE: an Intel GPU Accelerated Fully Homomorphic Encryption Library: A SYCL Sparkler: Making the Most of C++ and SYCL: summary, description and annotation

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This installment of a SYCL Sparkler explores in depth a way to implement a reasonably efficient implementation for Homomorphic Encryption using modern C++ with SYCL. As a result of their work, the authors learned some valuable optimization techniques and insights that the they have taken time to share in this very interesting and detailed piece.A key value of using C++ with SYCL, is the ability to be portable while supporting the ability to optimize at a lower level when it is deemed worth the effort. This work helps illustrate how the authors isolated that optimization work, and their thought process on how to pick what to optimize. The code for this implementation is available open source online. None of the performance numbers shown are intended to provide guidance on hardware selection. The authors offer their results and observations to illustrate the magnitude of changes that may correspond to the optimizations being discussed. Readers will find the information valuable to motivate their own optimization work on their applications using some of the techniques highlighted by these authors.Key Insights shared include: pros/cons of a hand-tuned vISA, memory allocation overheads, multi-tile scaling, event-based profiling, algorithm tuning, measuring of device throughput, developing with dualities to increase portability and performance portability.In a SYCL Sparkler, developers share details of their implementations as well as key insights (lessons learned). They discuss not only what worked, but what did not workat least what did not work initially. Learning effective use of any programing technique is boosted by learning the best thought processes to achieve programming results, and this is boosted by learning from the successes and false starts that other experts experience on the road to success. We appreciate the honesty of the authors in exposing their learnings, and happily include such discussions here that would not be explored in depth in most publications.In this piece, the authors share learnings from a project to create and optimize a SYCL-based GPU backend for Microsoft SEAL. Multiple optimizations are discussed including organizing to benefit from local memory, instruction optimization for modular addition and multiplication operations, and reduce memory allocation costs. Their insights are invaluable lessons discussed in this fascinating implementation. The authors also explore how having two GPU architectures (tiled and not tiled) help them tune their code to be more portable, and that supporting another duality (Linux and Windows) proved invaluable in expanding the test coverage substantially making our code robust sooner.Homomorphic Encryption (HE) is an emerging encryption scheme that allows computations to be performed directly on encrypted messages. This property provides promising applications such as privacy-preserving deep learning and cloud computing. Prior works have been proposed to enable practical privacy-preserving applications with architectural-aware optimizations on CPUs, CUDA-enabled GPUs, and FPGAs. However, there was no systematic optimization for the whole HE pipeline on Intel GPUs. We present the first-ever SYCL-based GPU backend for Microsoft SEAL APIs. We perform optimizations from instruction level, algorithmic level, and application level to accelerate our HE library based on the Cheon, Kim, Kim, and Song (CKKS) scheme on Intel GPUs. The performance is validated on two experimental (non-production) Intel GPUs.

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Table of Contents
Index
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XeHE: An Intel GPU Accelerated Fully Homomorphic Encryption Library

Alexander Lyashevsky, Intel Corporation, Santa Clara, CA, USA
Alexey Titov, Intel Corporation, Santa Clara, CA, USA
Yiqin Qiu, Intel Corporation, Santa Clara, CA, USA
Yujia Zhai, University of California, Riverside, CA, USA
Edited by James Reinders with assistance from Henry Gabb and John Pennycook
ISBN-13 (pbk): 979-8-88638-000-2
ISBN-13 (electronic): 979-8-88638-001-9
Copyright 2023 by Codeplay Software, an independently managed wholly owned subsidiary of Intel Corporation
This book is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit ofthe trademark owner, with no intention of infringement of the trademark.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.
SYCL and the SYCL logo are trademarks of the Khronos Group Inc. in the U.S. and/or other countries. Intel, the Intel logo, Intel Optane, and Xeon are trademarks of Intel Corporation in the U.S. and/or other countries. Khronos and the Khronos Group logo are trademarks of the Khronos Group Inc. in the U.S. and/or other countries. OpenCL and the OpenCL logo are trademarks of Apple Inc. in the U.S. and/or other countries. OpenMP and the OpenMP logo are trademarks of the OpenMP Architecture Review Board in the U.S. and/or other countries. While the advice and information in this book are believed to be true and accurate atthe date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The authors, editors, copyright holder, and publisher make no warranty, express or implied, with respect to the material contained herein.
Any source code or other supplementary material referenced is available to readers online. To find exactly where, visit the sparklers directory at https://github.com/syclsparklers/directory/blob/main/README.md (or use the tinyurl link: https://tinyurl.com/syclsparklers ).

Acknowledgements

We would like to acknowledge and thank our friends and colleagues for their inputs, advice, discussions, and help in this project:Fabian Boemer, Rosario Cammarota, Volley Chen, Srinivas Chennupaty, Fangwen Fu, Allison Gehrke, Hong Jiang, Mohannad Ibrahim, and Lian Tang.

Preface

Welcome to this installment in a series of detailed pieces that share insights gained in real worldprojects using C++ with SYCL. This series is known as SYCL Sparklersthanks to theirability to enlighten us in programming C++ with SYCL.

In a SYCL Sparkler, developers share details of their implementations as well as key insights(lessons learned). They discuss not only what worked, but what did not workat least what didnot work initially. Learning effective use of any programing technique is boosted by learning thebest thought processes to achieve programming results, and this is boosted by learning from thesuccesses andfalse starts that other experts experience on the road to success. We appreciate thehonesty of the authors in exposing their learnings, and happily include such discussions here thatwould not be explored in depth in most publications.

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