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检索条件"任意字段=Proceedings of the first international symposium on Databases in parallel and distributed systems"

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Optimized GPU Implementation of Grid Refinement in Lattice Boltzmann Method 38

Optimized GPU Implementation of Grid Refinement in Lattice B...

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international parallel and distributed Processing symposium (IPDPS)

作者： Mahmoud, Ahmed H. Salehipour, Hesam Meneghin, Massimiliano Autodesk Res Montreal PQ Canada Univ Calif Davis Davis CA USA

ISBN: (纸本)9798350387117;9798350387124

Nonuniform grid refinement plays a fundamental role in simulating realistic flows with a multitude of length scales. We introduce the first GPU-optimized implementation of this technique in the context of the lattice Boltzmann method. Our approach focuses on enhancing GPU performance while minimizing memory access bottlenecks. We employ kernel fusion techniques to optimize memory access patterns, reduce synchronization overhead, and minimize kernel launch latencies. Additionally, our implementation ensures efficient memory management, resulting in lower memory requirements compared to the baseline LBM implementations that were designed for distributed systems. Our implementation allows simulations of unprecedented domain size (e.g., 1596 x 840 x 840) using a single A100-40 GB GPU thanks to enabling grid refinement capabilities on a single GPU. We validate our code against published experimental data. Our optimization improves the performance of the baseline algorithm by 1.3-2X. We also compare against state-of-the-art current solutions for grid refinement LBM and show an order of magnitude speedup.

关键词： parallel GPU Simulation LBM Boltzmann Refinement

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Exploiting Inter-Layer Expert Affinity for Accelerating Mixture-of-Experts Model Inference 38

Exploiting Inter-Layer Expert Affinity for Accelerating Mixt...

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international parallel and distributed Processing symposium (IPDPS)

作者： Yao, Jinghan Anthony, Quentin Shafi, Aamir Subramoni, Hari Panda, Dhabaleswar K. (DK) Ohio State Univ Columbus Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9798350387117;9798350387124

In the realm of large language models (LLMs) like the Generative Pre-trained Transformer (GPT), the Mixture of Experts (MoE) paradigm has emerged as a powerful technique for enhancing model expressiveness and accuracy. However, the deployment of GPT MoE models for parallel inference on distributed systems presents significant challenges, primarily due to the extensive Alltoall communication required for expert routing and aggregation. This communication bottleneck exacerbates the already complex computational landscape, hindering the efficient utilization of high-performance computing resources. In this paper, we propose a lightweight optimization technique called ExFlow, to largely accelerate the inference of these MoE models. We take a new perspective on alleviating the communication overhead by exploiting the inter-layer expert affinity. Unlike previous methods, our solution can be directly applied to pre-trained MoE models without any fine-tuning or accuracy degradation. By proposing a context-coherent expert parallelism on distributed systems, our ExFlow design only uses one Alltoall communication to deliver the same functionality while previous methods all require two Alltoalls. By carefully examining the conditional probability in tokens' routing across multiple layers, we proved that pre-trained GPT MoE models implicitly exhibit a strong inter-layer expert affinity. We then design an efficient integer programming model to precisely capture such features and show that by properly placing the experts on corresponding GPUs, we can reduce up to 67% of tokens' cross-GPU routing latency on various hardware configurations and topologies. Our solution beats the cutting-edge Deepspeed-MoE in GPT MoE models with experts from 8 to 64, with up to 2.2x improvement in inference throughput. To the best of our knowledge, this is the first work in leveraging inter-layer expert affinity to accelerate the inference of GPT MoE models. We further provide a detailed study of how

关键词： Mixture of experts parallel inference Collective communication Generative models distributed system

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Fast, Accurate and distributed Simulation of novel HPC systems incorporating ARM and RISC-V CPUs 24

Fast, Accurate and Distributed Simulation of novel HPC syste...

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33rd international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Tampouratzis, Nikolaos Papaefstathiou, Ioannis EXAPSYS Plc Exascale Performance Syst Iraklion Greece

ISBN: (纸本)9798400704130

The growing developments of HPC systems used in a plethora of domains (healthcare, financial services, government and defense, energy) triggers an urgent demand for simulation frameworks that can simulate, in an integrated manner, both processing and network components of an HPC system-under-design (SuD). The main problem, however, is that, currently, there is a shortage of simulation frameworks that can handle the simulation of actual HPC systems, including the hardware, complete software stack and network dynamics in an integrated manner. In this work we start from the first known, open-source, fully-distributed Cloud simulation framework, COSSIM, and, as part of the RED-SEA1 and Vitamin-V2 European projects, we extend it so as to be able to accurately simulate HPC systems. The extended simulator has been evaluated when executing the very-widely used HPCG & LAMMPS benchmarks on both ARM & RISC-V architectures;the results demonstrate that the presented approach has up to 95% accuracy in the reported SuD aspects.

关键词： HPC Simulator distributed systems Simulator ARM RISC-V

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A parallel Partial Merge Repair Algorithm for Multi-block Failures for Erasure Storage systems 38

A Parallel Partial Merge Repair Algorithm for Multi-block Fa...

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international parallel and distributed Processing symposium (IPDPS)

作者： Zhang, Shuaipeng Li, Shiyi Wu, Chentao Wu, Ruobin Long, Saiqin Xia, Wen Harbin Inst Technol Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Jinan Univ Jinan Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence T Shenzhen Peoples R China Wuhan Natl Lab Optoelect Wuhan Peoples R China

ISBN: (纸本)9798350387117;9798350387124

In order to achieve high availability and low storage costs in distributed storage systems, erasure code is widely used instead of replication. Compared to replication, erasure code can reduce storage costs, but also brings higher repair costs. There are currently many repair algorithms to reduce the block reconstruction time of single block failure. However, applying the existing methods to multi-block failures may lead to unbalanced network traffic, unnecessary network transfers, and network congestion at data collection node during the repair process, which can not make full use of the bandwidth between nodes. To solve this problem, we propose a novel repair algorithm called Partial Merge Repair (PMR) for multi-block failures, which is a scheduling algorithm that considers network load between nodes and combines multiple failed blocks to recover together. It first divides all surviving nodes into different groups, and then the data collection nodes within the group collect the data needed to repair multiple blocks through cross merging. Finally, the data collection node sends the collected blocks to the repair node to complete the repair. Our study presents a formal definition and proof of network transfer time in the modeled repair process of PMR, highlighting its superior efficiency compared to existing methods in homogeneous environments. We implement a prototype of PMR to evaluate its performance. The experimental results indicate that compared to existing repair technologies, PMR improves repair throughput by 28%-256% for various scenes.

关键词： Erasure code distributed system Network transfer Repair algorithm parallelism

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Accelerating Function-Centric Applications by Discovering, Distributing, and Retaining Reusable Context in Workflow systems 24

Accelerating Function-Centric Applications by Discovering, D...

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33rd international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Thanh Son Phung Thomas, Colin Ward, Logan Chard, Kyle Thain, Douglas Univ Notre Dame Notre Dame IN 46556 USA Argonne Natl Lab Lemont IL USA Univ Chicago Chicago IL USA

ISBN: (纸本)9798400704130

Workflow systems provide a convenient way for users to write large-scale applications by composing independent tasks into large graphs that can be executed concurrently on high-performance clusters. In many newer workflow systems, tasks are often expressed as a combination of function invocations in a high-level language. Because necessary code and data are not statically known prior to execution, they must be moved into the cluster at runtime. An obvious way of doing this is to translate function invocations into self-contained executable programs and run them as usual, but this brings a hefty performance penalty: a function invocation now needs to piggyback its context with extra code and data to a remote node, and the remote node needs to take extra time to reconstruct the invocation's context before executing it, both detrimental to lightweight short-running functions. A better solution for workflow systems is to treat functions and invocations as first-class abstractions: subsequent invocations of the same function on a worker node should only pay for the cost of context setup once and reuse the context between different invocations. The remaining problems lie in discovering, distributing, and retaining the reusable context among workers. In this paper, we discuss the rationale and design requirement of these mechanisms to support context reuse, and implement them in TaskVine, a data-intensive distributed framework and execution engine. Our results from executing a large-scale neural network inference application and a molecular design application show that treating functions and invocations as first-class abstractions reduces the execution time of the applications by 94.5% and 26.9%, respectively.

关键词： Workflow systems Serverless Computing distributed Storage Burst Buffers

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Performance Analysis and Optimization of Nvidia H100 Confidential Computing for AI Workloads 22

Performance Analysis and Optimization of Nvidia H100 Confide...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tan, Yifan Mi, Zeyu Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University China

ISBN: (纸本)9798331509712

NVIDIA's H100 Confidential Computing (CC) counters the security hazards inherent in cloud AI workloads. It enforces data encryption to achieve data confidentiality, which leads to substantial throughput reductions as high as 93% in various AI workloads (such as TensorRT, PEFT and vLLM). Confronting this substantial overhead issue, we first delve into the underlying causes through meticulous analysis. This groundwork enables us to devise an innovative runtime system that operates seamlessly in the background, completely transparent to end-users. The cornerstone of our system lies in leveraging multiple encryption workers. Experiments demonstrate that our solution effectively reduces throughput drop to less than 28.1%. © 2024 IEEE.

关键词： Confidential Computing GPU H100 LLM

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Early Exploration of Using ChatGPT for Log-based Anomaly Detection on parallel File systems Logs 23

Early Exploration of Using ChatGPT for Log-based Anomaly Det...

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Egersdoerfer, Chris Zhang, Di Dai, Dong Univ N Carolina Charlotte NC 27599 USA

ISBN: (纸本)9798400701559

Log-based anomaly detection has been extensively studied to help detect complex runtime anomalies in production systems. However, existing techniques exhibit several common issues. first, they rely heavily on expert-labeled logs to discern anomalous behavior patterns. But labelling enough log data manually to effectively train deep neural networks may take too long. Second, they rely on numeric model prediction based on numeric vector input which causes model decisions to be largely non-interpretable by humans which further rules out targeted error correction. In recent years, we have witnessed groundbreaking advancements in large language models (LLMs) such as ChatGPT. These models have proven their ability to retain context and formulate insightful responses over entire conversations. They also present the ability to conduct few-shot and in-context learning with reasoning ability. In light of these abilities, it is only natural to explore their applicability in understanding log content and conducting anomaly classification among parallel file system logs.

关键词： anomaly detection parallel file system large language model

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sNPU: Trusted Execution Environments on Integrated NPUs 51

sNPU: Trusted Execution Environments on Integrated NPUs

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ACM/IEEE 51st Annual international symposium on Computer Architecture (ISCA)

作者： Feng, Erhu Feng, Dahu Du, Dong Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst SEIEE Shanghai Peoples R China Tsinghua Univ Dept Precis Instrument Beijing Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Beijing Peoples R China Chinese Acad Sci Key Lab Syst Software Beijing Peoples R China

ISBN: (纸本)9798350326598;9798350326581

Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-only TEEs to FPGA and GPU TEE systems. However, existing TEE systems exhibit inadequate and inefficient support for an emerging (and significant) processing unit, NPU. For instance, commercial TEE systems resort to coarse-grained and static protection approaches for NPUs, resulting in notable performance degradation (10%-20%), limited (or no) multitasking capabilities, and suboptimal resource utilization. In this paper, we present a secure NPU architecture, known as sNPU, which aims to mitigate vulnerabilities inherent to the design of NPU architectures. first, sNPU proposes NPU Guarder to enhance the NPU's access control. Second, sNPU defines new attack surfaces leveraging in-NPU structures like scratchpad and NoC, and designs NPU Isolator to guarantee the isolation of scratchpad and NoC routing. Third, our system introduces a trusted software module called NPU Monitor to minimize the software TCB. Our prototype, evaluated on FPGA, demonstrates that sNPU significantly mitigates the runtime costs associated with security checking (from upto 20% to 0%) while incurring less than 1% resource costs.

关键词： Accelerator Machine learning Security

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LBCB: One-sided RDMA-based distributed B+ tree Index with Low Bandwidth Consumption 22

LBCB: One-sided RDMA-based Distributed B+ tree Index with Lo...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Liu, Jibo Xi, Rui Cao, Qinzhen Nie, Xiaowen Hou, Zhuohan University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331509712

Disaggregated memory architecture segregates computing and memory resources into distinct pools interconnected by a high-speed one-sided RDMA (Remote Direct Memory Access) network, enhancing memory utilization, reducing costs, and facilitating elastic scaling of computing and memory resources. However, optimizing index structures to maximize the benefits of this framework poses significant challenges. Despite progress in B+ tree indexes for disaggregated memory systems, they still suffer from severe write and read amplification issues, which impede latency and throughput *** this paper, we propose LBCB, a B+ tree index for disaggregated memory, which significantly reduces the bandwidth consumption of index operations. first, LBCB introduces an RDMA friendly B+ tree leaf node structure, which improves concurrency while reducing the bandwidth consumption. Second, LBCB designs a logical fusion FAA lock that synchronizes more information with a single RDMA communication, significantly reducing the number of RDMA network round trips. Finally, LBCB optimistically compresses the critical path of index operations, further optimizing latency and throughput. Evaluation results show that compared with the state-of-the-art distributed B+ tree index, the write performance of LBCB is improved by 1.42 times. © 2024 IEEE.

关键词： Memory architecture

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Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline parallelism 23

Redundancy-Free High-Performance Dynamic GNN Training with H...

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Xia, Yaqi Zhang, Zheng Wang, Hulin Yang, Donglin Zhou, Xiaobo Cheng, Dazhao Wuhan Univ Sch Comp Sci Wuhan Peoples R China Nvidia Corp Santa Clara CA USA Univ Macau IOTSC Macau Peoples R China

ISBN: (纸本)9798400701559

Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to dynamic graphs. distributed TGNN training requires efficiently tackling temporal dependency, which often leads to excessive cross-device communication that generates significant redundant data. However, existing systems are unable to remove the redundancy in data reuse and transfer, and suffer from severe communication overhead in a distributed setting. This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems. Exploiting dependency patterns of TGNN models and characteristics of dynamic graph datasets, we design redundancy-free data organization and load-balancing partitioning strategies that mitigate the redundant data communication and evenly partition dynamic graphs at the vertex level. Furthermore, we develop a hierarchical pipeline mechanism integrating data prefetching, micro-batch pipelining, and asynchronous pipelining to mitigate the communication overhead. As the first scaling study on the memory-based TGNNs training, experiments conducted on an HPC cluster of 64 GPUs show that Sven can achieve up to 1.7x-3.3x speedup over the state-of-art approaches and a factor of up to 5.26x communication efficiency improvement.

关键词： distributed training dynamic GNN pipeline parallelism

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