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PhD Forum: Efficient Privacy-Preserving Processing via Memory-Centric Computing

PhD Forum: Efficient Privacy-Preserving Processing via Memor...

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Reliable distributed systems

作者： Mpoki Mwaisela University of Neuchâtel Neuchâtel Switzerland

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data movement overhead resulting from the underlying cryptographic algorithms impedes the adoption of these techniques in practice. Existing approaches focus on improving computational overhead using specialized hardware like GPUs and FPGAs, but these methods still suffer from the same processor-DRAM overhead. Novel hardware technologies that support in-memory processing have the potential to address this problem. Memory-centric computing, or processing-in-memory (PIM), brings computation closer to data by introducing low-power processors called data processing units (DPUs) into memory. Besides its in-memory computation capability, PIM provides extensive parallelism, resulting in significant performance improvement over state-of-the-art approaches. We propose a framework that uses recently available PIM hardware to achieve efficient privacy-preserving computation. Our design consists of a four-layer architecture: (1) an application layer that decouples privacy-preserving applications from the underlying protocols and hardware; (2) a protocol layer that implements existing secure computation protocols (HE and MPC); (3) a data orchestration layer that leverages data compression techniques to mitigate the data transfer overhead between DPUs and host memory; (4) a computation layer which implements DPU kernels on which secure computation algorithms are built.

关键词： Privacy Protocols Program processors Scalability Computer architecture parallel processing Hardware Multi-party computation Reliability Homomorphic encryption

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Performance Analysis of parallel FFT on Large Multi-GPU systems

Performance Analysis of Parallel FFT on Large Multi-GPU Syst...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Alan Ayala Stan Tomov Miroslav Stoyanov Azzam Haidar Jack Dongarra University of Tennessee Knoxville TN USA Oak Ridge National Laboratory Oak Ridge TN USA Nvidia Corporation Santa Clara CA USA Universiry of Manchester Manchester UK

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

In this paper we present a performance study of multidimensional Fast Fourier Transforms (FFT) with GPU accelerators on modern hybrid architectures, as those expected for upcoming exascale systems. We assess and leverage features from traditional implementations of parallel FFTs and provide an algorithm that encompasses a wide range of their parameters, and adds novel developments such as FFT grid shrinking and batched transforms. Next, we create a bandwidth model to quantify the computational costs and analyze the well-known communication bottleneck for All-to-All and Point-to-Point MPI exchanges. Then, using a tuning methodology, we are able to accelerate the FFT computation and reduce the communication cost, achieving linear scalability on a large-scale system with GPU accelerators. Finally, our performance analysis is extended to show that carefully tuning the algorithm can further accelerate applications heavily relying on FFTs, such is the case of molecular dynamics software. Our experiments were performed on Summit and Spock supercomputers with IBM Power9 cores, over 3000 NVIDIA V-100 GPUs, and AMD MI-100 GPUs.

关键词： Scalability Heuristic algorithms Software algorithms Graphics processing units Bandwidth Transforms Supercomputers

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Implementing an Attack Graph Generator in CUDA 34

Implementing an Attack Graph Generator in CUDA

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Li, Ming Hawrylak, Peter J. Hale, John Univ Tulsa Tandy Sch Comp Sci Tulsa OK 74104 USA

ISBN: (纸本)9781728174457

Attack graphs can be used in security analysis for both computer networks and cyber physical systems to enumerate all possible attack paths. The main challenge in applying attack graphs is the design of scalable generators, which should construct large scale graphs within practical limits for execution time and storage resources. With the availability of high performance computing hardware, parallel computing units can be employed to accelerate the exploration of new states in attack graph generation. While NVIDIA's GPU devices have significantly improved the performance of other graph-oriented algorithms, there is little research using them on the generation of attack graphs. In this paper, we present a parallel attack graph generator based on NVIDIA's extension of C language: CUDA. To accelerate the exploration, the generator uses a large number of warps in the GPU device. Each warp is assigned with multiple initial states, which are then expanded by 32 in-warp threads. The performance experiments are conducted on a virtual machine instance providing a Tesla P4 GPU. The acceleration achieved demonstrates the effectiveness of using a large number of CUDA cores to accelerate the attack graph generator.

关键词： Cyber security attack graph parallel computing NVIDIA GPU CUDA

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OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC systems

OMB-Py: Python Micro-Benchmarks for Evaluating Performance o...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Nawras Alnaasan Arpan Jain Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive feature of Python is that it provides easy-to-use programming interface while allowing library developers to enhance performance of their applications by harnessing the computing power offered by High Performance Computing (HPC) platforms. Efficient communication is key to scaling applications on parallel systems, which is typically enabled by the Message Passing Interface (MPI) standard and compliant libraries on HPC hardware. mpi4py is a Python-based communication library that provides an MPI-like interface for Python applications allowing application developers to utilize parallel processing elements including GPUs. However, there is currently no benchmark suite to evaluate communication performance of mpi4py-and Python MPI codes in general-on modern HPC systems. In order to bridge this gap, we propose OMB-Py-Python extensions to the open-source OSU Micro-Benchmark (OMB) suite-aimed to evaluate communication performance of MPI-based parallel applications in Python. To the best of our knowledge, OMB-Py is the first communication benchmark suite for parallel Python applications. OMB-Py consists of a variety of point-to-point and collective communication benchmark tests that are implemented for a range of popular Python libraries including NumPy, CuPy, Numba, and PyCUDA. Our evaluation reveals that mpi4py introduces a small overhead when compared to native MPI libraries. We plan to publicly release OMB-Py to benefit the Python HPC community.

关键词： Performance evaluation Message passing Benchmark testing Programming parallel processing Market research Libraries

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MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-B...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Qihan Wang Bin Ren Jie Chen Robert G. Edwards Department of Computer Science William &#x0026 Mary Williamsburg VA Jefferson Lab Newport NewsVA

Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation-/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the data-intensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPU

关键词： distributed processing Correlation Tensors Quantum computing Scientific computing Scheduling algorithms Lattices

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Polyjuice: High-Performance Transactions via Learned Concurrency Control 15

Polyjuice: High-Performance Transactions via Learned Concurr...

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USENIX Annual Technical Conference / 15th USENIX symposium on Operating systems Design and Implementation (OSDI)

作者： Wang, Jiachen Ding, Ding Wang, Huan Christensen, Conrad Wang, Zhaoguo Chen, Haibo Li, Jinyang Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Shanghai Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781939133229

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.

关键词： Concurrency control

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A Scheduling Approach to Incremental Maintenance of Datalog Programs 34

A Scheduling Approach to Incremental Maintenance of Datalog ...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Singh, Shikha Madaminov, Sergey Bender, Michael A. Ferdman, Michael Johnson, Ryan Moseley, Benjamin Ngo, Hung Nguyen, Dung Olesen, Soeren Stirewalt, Kurt Washburn, Geoffrey Williams Coll Williamstown MA 01267 USA SUNY Stony Brook Stony Brook NY 11794 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Infor Inc New York NY USA Amazon Inc Seattle WA USA Relat AI Berkeley CA USA

ISBN: (纸本)9781728168760

In this paper, we study the problem of incremental maintenance of Datalog programs and model it as a scheduling problem on DAGs. We design provably good time- and memory-efficient scheduling algorithms for (re)executing a Datalog program where some (but not necessarily all) of the inputs have changed. We prove that our schedulers, called LevelBased and LevelBased with lookahead, have asymptotically improved running time and space efficiency when compared with benchmark algorithms used in production at LogicBlox. The main result of the paper is a hybrid scheduler, which combines LevelBased with the production LogicBlox scheduler (or any other heuristic scheduler). The hybrid scheduler achieves strong worst-case guarantees and robustness without losing out on the best-case behavior of the production LogicBlox scheduler. Our experiments show that the hybrid scheduler results in similar or improved total execution times compared to LogicBlox scheduler, while consistently reducing the scheduling overhead-by as much as 50% on some datasets. This hybrid scheme requires little to no overhead but provides predictability and reliability, which are crucial in a commercial application such as LogicBlox.

关键词： Datalog programs incremental maintenance DAG scheduling parallel task scheduling databases incremental computing LogicBlox

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A Pilot Study of 10-Day Composites From the NASA Harmonized Landsat and Sentinel-2 Images for Terrestrial Monitoring

A Pilot Study of 10-Day Composites From the NASA Harmonized ...

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Junchang Ju Christopher S.R. Neigh Brian Freitag Qiang Zhou Madhu Sridhar Jeffrey Masek University of Maryland College Park Maryland USA NASA Goddard Space Flight Center Greenbelt Maryland USA NASA Marshall Space Flight Center Huntsville Alabama USA Science Systems and Applications Inc (SSAI) Lanham Maryland USA University of Alabama Huntsville Alabama USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

To meet the demand for more frequent medium resolution land observation imagery, the United States National Aeronautics and Space Administration’s Harmonized Landsat and Sentinel-2 (HLS) project creates comparable 30-m surface reflectance data products from Landsat 8/9 and Sentinel-2A/B Top-of-Atmosphere measurements, by applying a series of harmonization algorithms that include atmospheric correction, cloud masking, view angle effect normalization, bandpass adjustment on Sentinel-2, and gridding in the same spatial reference system. Typically, over a hundred HLS images are available in a year for any location when all four sensors are in operation. Although cloud and cloud shadow pixels are masked in the HLS, they are retained in the distributed data, resulting in unnecessarily large data volume, and there are also a small proportion of cloud and cloud shadow pixels that were undetected. The high data volume and cloud mask omission problem combine to impose a challenge for many applications. In this pilot study we produce 10-day image composites from individual HLS observations. To eliminate the residual cloud and cloud shadow pixels, our composite algorithm takes advantage of the aerosol information derived during the atmospheric correction, the distance to the detected cloud and cloud shadow, and a few spectral screening rules. Following the screening of the observations, a maximal value Enhanced Vegetation Index 2 (EVI2) rule is used to select the best observation for each pixel location. The 10-day HLS image composites were created globally for a few growing season months, and at selected location for a few years. They are in general free of cloud and cloud shadow contamination and spatially complete, with a reduced data volume and increased overall data quality. The composited HLS images are made freely available in NASA's Visualization, Exploration, and Data Analysis (VEDA) environment for users’ evaluation and feedback on the composite window length and data

关键词： Reflectivity Atmospheric measurements Clouds Data integrity NASA distributed databases Landsat

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Secure Peer to Peer Learning Using Auto Encoders

Secure Peer to Peer Learning Using Auto Encoders

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IEEE international symposium on Smart Electronic systems (iSES)

作者： Anirudh Kasturi Akshat Agrawal Chittaranjan Hota Dept. of CSIS BITS Pilani Hyderabad India Dept. of EEE BITS Pilani Hyderabad India

ISBN: (纸本)9798350399233

We need machine learning algorithms that can use clients' data to create tailored models while adhering to tight privacy rules because people are concerned about privacy as more and more personal devices are being connected to the internet. Here, we present a robust method with a provable convergence rate to solve the issue, as mentioned earlier, in a distributed (peer-to-peer) asynchronous setting. This paper introduces a generic framework that trains an autoencoder on each client and then transmits the encoded data to the remaining clients. In a single round of communication, each client compiles data from several clients and trains a deep-learning model on the encoded data. In this work, we detail the architecture and implementation of the proposed approach and assess its efficacy in light of its comparison to the best-in-class central training and federated learning (FL) algorithms. Compared to FL and central training algorithms, our experiments on various datasets reveal that our proposed technique yields comparable accuracy. Compared to FL, our results demonstrate that the suggested approach reduces the quantity of data transfer by over 95 %, saving a significant amount of bandwidth.

关键词： Training Data privacy Computer aided instruction Machine learning algorithms Distance learning Federated learning distributed databases

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parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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