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Automatic Generation of distributed-Memory Mappings for Tensor Computations 23

Automatic Generation of Distributed-Memory Mappings for Tens...

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2023 international conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Kong, Martin Abu Yosef, Raneem Rountev, Atanas Sadayappan, P. Ohio State University Columbus United States University of Utah Salt Lake City United States

ISBN: (纸本)9798400701092

While considerable research has been directed at automatic parallelization for shared-memory platforms, little progress has been made in automatic parallelization schemes for distributed-memory systems. We introduce an innovative approach to automatically produce distributed-memory parallel code for an important subclass of affine tensor computations common to Coupled Cluster (CC) electronic structure methods, neuro-imaging applications, and deep learning *** propose a novel systematic approach to modeling the relations and trade-offs of mapping computations and data onto multidimensional grids of homogeneous nodes. Our formulation explores the space of computation and data distributions across processor grids. Tensor programs are modeled as a non-linear symbolic formulation accounting for the volume of data communication and per-node capacity constraints induced under specific mappings. Solutions are found, iteratively, using the Z3 SMT solver, and used to automatically generate efficient MPI code. Our evaluation demonstrates the effectiveness of our approach over distributed-Memory Pluto and the Cyclops Tensor Framework. © 2023 Owner/Author(s).

关键词： Tensors

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PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption 25

PipeLLM: Fast and Confidential Large Language Model Services...

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30th international conference on Architectural Support for Programming Languages and Operating systems-ASPLOS

作者： Tan, Yifan Tan, Cheng Mi, Zeyu Chen, Haibo Shanghai Jiao Tong Univ SEIEE Inst Parallel & Distributed Syst Shanghai Peoples R China Northeastern Univ Boston MA USA

ISBN: (纸本)9798400706981

Confidential computing on GPUs, like NVIDIA H100, mitigates the security risks of outsourced Large Language Models (LLMs) by implementing strong isolation and data encryption. Nonetheless, this encryption incurs a significant performance overhead, reaching up to 52.8% and 88.2% throughput drop when serving OPT-30B and OPT-66B, respectively. To address this challenge, we introduce PipeLLM, a user-transparent runtime system. PipeLLM removes the overhead by overlapping the encryption and GPU computation through pipelining-an idea inspired by the CPU instruction pipelining-thereby effectively concealing the latency increase caused by encryption. The primary technical challenge is that, unlike CPUs, the encryption module lacks prior knowledge of the specific data needing encryption until it is requested by the GPUs. To this end, we propose speculative pipelined encryption to predict the data requiring encryption by analyzing the serving patterns of LLMs. Further, we have developed an efficient, low-cost pipeline relinquishing approach for instances of incorrect predictions. Our experiments show that compared with vanilla systems without confidential computing (e.g., vLLM, PEFT, and FlexGen), PipeLLM incurs modest overhead (<19.6% in throughput) across various LLM sizes, from 13B to 175B. PipeLLM's source code is available at https://***/SJTU-IPADS/PipeLLM.

关键词： Nvidia Confidential Computing Large Language Model Confidential Virtual Machine

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Evaluating Data-parallel distributed Training Strategies 14

Evaluating Data-Parallel Distributed Training Strategies

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14th international conference on COMmunication systems and NETworkS, COMSNETS 2022

作者： Ponnuswami, Ganesan Kailasam, Sriram Dinesh, Dileep Aroor Indian Institute of Technology School of Computing and Electrical Engineering Himachal Pradesh Mandi India

ISBN: (纸本)9781665421041

Deep neural networks (DNNs) require distributed training strategies to deal with large data sizes. TensorFlow is one of the most widely used frameworks that support distributed training. Among the TensorFlow training strategies, the mirrored strategy (MS) and multi-worker mirrored strategy (MWMS) follow synchronous distributed model training;the parameter server strategy (PSS) implements asynchronous methods for training. In this work, we analyze the performance of distributed training strategies in synchronous and asynchronous settings by implementing LSTM and MLP on two industrial time-series datasets from network management systems (NMS) and Google cluster trace (GCT), and Inception-v3 model on CIFAR-10 image dataset. The strategies were compared by empirically evaluating loss, accuracy, training time, and scalability. © 2022 IEEE.

关键词： Long short-term memory

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An Efficient parallel Adaptive GMG Solver for Large-Scale Stokes Problems 29th

An Efficient Parallel Adaptive GMG Solver for Large-Scale St...

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29th international conference on parallel and distributed Computing (Euro-Par)

作者： Saberi, S. Meschke, G. Vogel, A. Ruhr Univ Bochum High Performance Comp Univ Str 150 D-44801 Bochum Germany Ruhr Univ Bochum Inst Struct Mech Univ Str 150 D-44801 Bochum Germany

ISBN: (纸本)9783031396977;9783031396984

We study the performance and scalability of the adaptive geometric multigrid method with the recently developed restricted additive Vanka (RAV) smoother for the finite element solution of large-scale Stokes problems on distributed-memory clusters. A comparison of the RAV smoother and the classical multiplicative and additive Vanka smoothers is presented. We present three cache policies for the smoother operators that provide a balance between cached and on-the-fly computation and discuss their memory footprint and computational cost. It is shown that the restricted additive smoother with the most efficient cache policy has the smallest memory footprint and is computationally cheaper in comparison with the other smoothers and can, therefore, be used for large-scale problems even when the available main memory is constrained. We discuss the parallelization aspects of the smoother operators and show that the RAV operator can be replicated exactly in parallel with a very small communication overhead. We present strong and weak scaling of the GMG solver for 2D and 3D examples with up to roughly 540 million degrees of freedom on up to 2048 MPI processes. The GMG solver with the restricted additive smoother is shown to achieve rapid convergence rates and scale well in both the strong and weak scaling studies, making it an attractive choice for the solution of large-scale Stokes problems on HPC systems.

关键词： Multigrid Stokes flow Finite element method Massively parallel

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Performance of the Modern parallel Programming Approaches: A Case Study 18

Performance of the Modern Parallel Programming Approaches: A...

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18th IEEE international conference on Computer Science and Information Technologies, CSIT 2023

作者： Fedynyak, Volodymyr Hryniv, Oleksa Vey, Bohdan Farenyuk, Oleg Ukrainian Catholic University Faculty of Applied Sciences L'viv Ukraine Institute for Condensed Matter Physics National Academy of Sciences of Ukraine L'viv Ukraine

ISBN: (纸本)9798350360462

This research is devoted to a quantitative comparison of the performance of several parallel programming approaches and compares their computational performance. Comparison is performed for the Computational Dynamics Problem solved by the MacCormack scheme. parallel computation properties of this sample problem task are well-understood. The parallel programming techniques were chosen considering the recent trends in high-performance computing. Both high-level framework-based implementations (using OneAPI DPC++ and ArrayFire) and low-level implementations (based on CUDA C++) are reviewed and their performance is compared. Additionally, single SMP systems with multiple CUDA-capable GPUs were studied using GPUDirect and Unified Memory technologies. Wall-time was used as a performance metric. The comparison was performed using Student's t-test for the Gauss-distributed experimental results and the non-parametric Wilcoxon signed-rank test - for other distributions. The results show that CUDA-based solutions outperform other approaches though development time considerations often can favor more high-level approaches. © 2023 IEEE.

关键词： Graphics processing unit

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A distributed Storage System for System Logs Based on Hybrid Compression Scheme 21

A Distributed Storage System for System Logs Based on Hybrid...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chang, Baoming Zhou, Fengxi Wang, Zhaoyang Wen, Yu Zhang, Boyang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

关键词： Digital storage

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14th international conference on parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304415

The proceedings contain 77 papers. The special focus in this conference is on parallel Processing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection Methods;distributed Work Stealing in a Task-Based Dataflow Runtime;task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices;high Performance Dataframes from parallel Processing Patterns;global Access to Legacy Data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel parallel Approach for Modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy Measurement on Heterogeneous systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl

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14th international conference on parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304446

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RePoSt: distributed Self-Reconfiguration Algorithm for Modular Robots Based on Porous Structure

RePoSt: Distributed Self-Reconfiguration Algorithm for Modul...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Bassil, Jad Piranda, Benoit Makhoul, Abdallah Bourgeois, Julien Univ Bourgogne Franche Comte CNRS FEMTO ST Inst 1 Cours Leprince Ringuet F-25200 Montbeliard France

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

In this paper, we propose a new self-reconfiguration scheme for modular robots based on a metamodule design that allows to form a 3D porous structure. The porous structure enables a parallel flow of modules inside it without blocking. The meta-module can also be used to fill its internal volume with an additional number of modules allowing the structure to be compressible and expandable. Hence, it is a potential for improving the self-reconfiguration process. We first present the meta-module model and the porous structure built using it. Then, we describe an algorithm to self-reconfigure the structure from an initial shape to a given goal shape. We evaluated the algorithm in simulation on structures composed of up to 2,700 modules. We studied the performance in term of parallelism, showed that the number of communications is proportional to the number of motions and the execution time varies linearly with the diameter of the configuration.

关键词： Three-dimensional displays Shape parallel processing Intelligent robots

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Reliability on distributed System Considering Transmission Lines 5

Reliability on Distributed System Considering Transmission L...

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5th international conference on Power and Energy Applications, ICPEA 2022

作者： Hu, Yishuang Bo, Yimin Hangzhou City University School of Information and Electrical Engineering Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665487610

Considering the highly penetration of distributed generators, the distribution system has been extensively studied in recent year. However, traditional reliability evaluation algorithms require huge computational resources when the number of distributed generators is large. Therefore, in this paper, similar to series-parallel systems, a distributed seriesparallel system is defined to release the computational burden. In this system, the interaction between the basic components and distributed components is concerned and the structure between these components is described as a series-parallel component. The uniqueness structures of distributed series-parallel system have been taken into account and two analytical reliability assessment techniques in terms of two scenarios are defined. The relationships between series-parallel components are quantified and the distributed generators are equivalent as ordinal generators. The comparisons on accuracy and efficiency of the proposed methods with traditional methods are demonstrated. Case studies are conducted to verify the proposed models. © 2022 IEEE.

关键词： Reliability

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