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P-AES: Advanced Encryption Standard parallel Optimization on MGPUSim

P-AES: Advanced Encryption Standard Parallel Optimization on...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jiawei Qin Yaobin Wang Ling Li Shuang Yang Xiaorong Zhang School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Advanced Encryption Standard (AES), as one of the most popular encryption algorithms, has been widely studied on single GPU and CPU. However, the research on multi-GPU platforms is not deep enough, and with the rapid increase in data size, it is difficult for single-GPU platforms to meet the demand for high-performance computing. To solve this problem, this paper presents a novel AES parallelization study "P-AES" on MGPUSim, including its parallel execution mechanism as well as architectural design. In addition, we modify the instruction set of MGPUSim along with thread optimization to save memory overhead, increase data access speed, and improve system performance by loading S-boxes and key extension arrays from global memory to shared memory. The experimental results show that: (1) P-AES performs well on MGPUSim, achieving an average speedup of 1.5×-1.7× compared to the AES implementation using CUDA kernels, and a speedup of 2.75× compared to M-AES (the unoptimized AES on MGPUSim). (2) By conducting encryption experiments on plaintext data of varying sizes, we found that P-AES exhibits high stability and throughput. Compared to the modern sliced AES, the P-AES algorithm achieves a throughput of up to 812 Gbps, which is 1.3 times that of their implementation and 2.74 times that of M-AES.

关键词： Runtime Instruction sets System performance Memory management Graphics processing units Switches Throughput Encryption Standards Optimization

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation

Multi-view Hypergraph-based Self-supervised Learning Model f...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jiexun Shen Dongfan Li Wanjun Chen Yong Xiao Guosheng Kang Jianxun Liu Zhenlian Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.

关键词： Knowledge engineering Mashups Noise Neural networks Collaboration Service computing Self-supervised learning Feature extraction Data models Noise measurement

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distributed Aerial 3D Object Mapping Reconstruction Using Message Passing Interface

Distributed Aerial 3D Object Mapping Reconstruction Using Me...

引用

international Electronics symposium (IES)

作者： Cahyo Arissabarno Idris Winarno Sritrusta Sukaridhoto Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

Aerial reconstruction of 3D object mapping involves utilizing drones or Unmanned Aerial Vehicles (UAVs) equipped with cameras to capture images for high-resolution 3D model reconstruction of various objects and terrains. This technique is applicable across various fields, such as Urban Planning, Vehicle Navigation, Environmental Studies, Civil Engineering, and Cultural Heritage Conservation. Drones capture hundreds of overlapping aerial photos, which are then processed to generate photorealistic 3D representations of topographic surfaces using photogrammetry software. However, the reconstruction process is time-consuming and computationally intensive, especially when processing large datasets. This research addresses these challenges by employing parallel computing using the Message Passing Interface (MPI) to distribute the computational workload across multiple nodes. By utilizing MPI, the computational tasks are efficiently divided among four nodes, significantly reducing processing time and alleviating the burden on individual CPUs. The study demonstrates that parallel computing with MPI reduces the reconstruction time from 74 minutes to 27 minutes, achieving a speedup of 2.74 times. This research demonstrates a novel application of MPI to the domain of aerial 3D reconstruction, achieving a specific and substantial reduction in processing time. Additionally, it provides insights into future enhancements, such as parallel texture reconstruction and automation of translation processes.

关键词： Surface reconstruction Three-dimensional displays Message passing Urban planning parallel processing Software Surface topography

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4th Workshop on parallel AI and Systems for the Edge

4th Workshop on Parallel AI and Systems for the Edge

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

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Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Scaling Ensembles of Data-Intensive Quantum Chemical Calcula...

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

作者： Kshitij Mehta Massimiliano Lupo Pasini Stephan Irle Pilsun Yoo Frédéric Suter Dmitry Ganyushin Scott Klasky Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

关键词： Quantum computing Computational modeling High performance computing Predictive models Metadata Space exploration Computational efficiency

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FPChecker: Floating-Point Exception Detection Tool and Benchmark for parallel and distributed HPC

FPChecker: Floating-Point Exception Detection Tool and Bench...

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IEEE international symposium on Workload Characterization (IISWC)

作者： Laguna, Ignacio Tirpankar, Tanmay Li, Xinyi Gopalakrishnan, Ganesh Lawrence Livermore Natl Lab Livermore CA 94550 USA Univ Utah Salt Lake City UT 84112 USA

ISBN: (数字)9781665487986

ISBN: (纸本)9781665487986

Floating-point arithmetic is fundamental to many areas including high-performance computing and machine learning. In order to ensure the numerical integrity of the overall computation, numerical exceptions (such as NaNs) must be detected and suitably reported to the user. Unfortunately, today's best available methods and tools are not generalenough. Moreover, there are no comprehensive benchmarks today that can be compiled to intermediate representations such as LLVM and analyzed at that level. In this paper, we contribute the first such benchmark suite that spans five HPC proxy applications plus eight public benchmarks that run on CPU multicores under MPI, OpenMP, and performance portability layers. We also release our tool (also called LLFPX) that is up to 7.9x faster on many important benchmarks and overall more comprehensive with respect to exception variety coverage. This paper presents the tool, its design, and evaluation on our benchmarks, with the tool, benchmarks, and a facility to examine results on the web available for public use upon acceptance. Other result highlights include the effect of compiler optimizations on exceptions.

关键词： floating-point numerical consistency exceptions LLVM benchmarking CPU multicores

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JSSPP 2022 - 25th Workshop on Job Scheduling Strategies for parallel Processing

JSSPP 2022 - 25th Workshop on Job Scheduling Strategies for ...

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

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Evaluating Asynchronous parallel I/O on HPC Systems

Evaluating Asynchronous Parallel I/O on HPC Systems

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international symposium on parallel and distributed Processing (IPDPS)

作者： John Ravi Suren Byna Quincey Koziol Houjun Tang Michela Becchi North Carolina State University Lawrence Berkeley National Laboratory

parallel I/O is an effective method to optimize data movement between memory and storage for many scientific applications. Poor performance of traditional disk-based file systems has led to the design of I/O libraries which take advantage of faster memory layers, such as on-node memory, present in high-performance computing (HPC) systems. By allowing caching and prefetching of data for applications alternating computation and I/O phases, a faster memory layer also provides opportunities for hiding the latency of I/O phases by overlapping them with computation phases, a technique called asynchronous I/O. Since asynchronous parallel I/O in HPC systems is still in the initial stages of development, there hasn't been a systematic study of the factors affecting its *** this paper, we perform a systematic study of various factors affecting the performance and efficacy of asynchronous I/O, we develop a performance model to estimate the aggregate I/O bandwidth achievable by iterative applications using synchronous and asynchronous I/O based on past observations, and we evaluate the performance of the recently developed asynchronous I/O feature of a parallel I/O library (HDF5) using benchmarks and real-world science applications. Our study covers parallel file systems on two large-scale HPC systems: Summit and Cori, the former with a GPFS storage and the latter with a Lustre parallel file system.

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A High-performance Hardware Accelerator for Genome Alignment

A High-performance Hardware Accelerator for Genome Alignment

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Haoyu Liao Yuan Li Qiang Wang Mingche Lai Hui Su Ziwen Li Xingyun Qi School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Genome alignment is a vital process in genome sequencing and bio-informatics research. It entails aligning short DNA sequence fragments (usually tens to hundreds of base pairs) with a reference genome sequence, uncovering crucial biological information and variations. However, due to the high computational complexity of current sequence matching algorithms and the rapid growth of genetic data, there exists a computational bottleneck in sequence alignment workflows. Therefore, scholars have turned to using hardware to accelerate this computation, with a focus on high-performance computing. While existing acceleration solutions have mostly concentrated on the classical algorithm, showing significant improvements, there has been limited work on accelerating alignment algorithms in popular bio-informatics software tools (enhanced version). In this paper, we proposed a hardware accelerator for the alignment tasks in the popular sequence alignment tool minimap2 using the KSW2 algorithm. Our approach utilizes an anti-diagonal processing element(PE) array for parallel computation, implements the BAND technology in hardware, and enables support for longer input sequences without sacrificing accuracy. Additionally, we integrated the traceback stage of the KSW2 algorithm in hardware, reducing communication data overhead. We attained a 15.32x acceleration compared to software optimized with Streaming SIMD Extensions (SSE) instruction sets and hyper-threading technology. Additionally, our design shows a 3.42x speedup compared to other high-performance hardware.

关键词： Sequential analysis Single instruction multiple data Instruction sets High performance computing Software algorithms Genomics Bioinformatics Software tools Hardware acceleration Next generation networking

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Ex-NNQMD: Extreme-Scale Neural Network Quantum Molecular Dynamics

Ex-NNQMD: Extreme-Scale Neural Network Quantum Molecular Dyn...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Rajak, Pankaj Aditya, Anikeya Fukushima, Shogo Kalia, Rajiv K. Linker, Thomas Liu, Kuang Luo, Ye Nakano, Aiichiro Nomura, Ken-ichi Shimamura, Kohei Shimojo, Fuyuki Vashishta, Priya Argonne Natl Lab Argonne Leadership Comp Facil Lemont IL 60439 USA Univ Southern Calif Collaboratory Adv Comp & Simulat Los Angeles CA 90007 USA Kumamoto Univ Dept Phys Kumamoto Japan

ISBN: (纸本)9781665435772

Deep learning is revolutionizing countless scientific and engineering fields. In particular, SC20 Gordon Bell award represented a breakthrough in molecular simulation, i.e., 100-million-atom simulation with quantum-mechanical accuracy on the Summit supercomputer at ORNL, using deep potential molecular dynamics (MD). Moving forward, while these simulations were performed only in gentle equilibrium conditions, far-from-equilibrium MD simulation involving light-induced electronic excited states finds numerous scientific and engineering applications. However, it remains a challenge to perform such far-from-equilibrium simulations at larger spatiotemporal scales, where growing number of unphysical predictions of interatomic force prohibits simulations involving larger numbers of atoms for longer times. In this paper, we propose a physically-based inductive bias, maximally-preserved Maxwell-Boltzmann (MPMB), to overcome this fidelity-scaling problem. Along with hybrid divide-and-conquer parallelization and single-node level optimization using multithreading and data parallel SIMD, the resulting Ex-NNQMD (extreme-scale neural network quantum molecular dynamics) algorithm has achieved unprecedented scales of far-from-equilibrium simulations: 1) 5.1-billion atom system with a parallel efficiency of 0.94, and 2) a sustained performance of 6.4 nanoseconds/day for 10-million atom system both on 262,144 cores of the Theta supercomputer at Argonne Leadership computing Facility. Extended fidelity scaling and efficient parallelization have allowed us for the first time to study light-induced ferroelectric switching under extreme electronic excitation at experimentally relevant spatiotemporal scales with accuracy.

关键词： neural network molecular dynamics parallel computing light-induced electronic excited state

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