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A LLM Checkpointing System of the Hybrid Memory architecture 22

A LLM Checkpointing System of the Hybrid Memory Architecture

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22nd IEEE international symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Chen, Gengbin Zhu, Yuan Li, Dingding Tang, Yong South China Normal University School of Computer Science Guangzhou510631 China

ISBN: (纸本)9798331509712

The training process of large language models (LLMs) sometimes may raise an error, often necessitating the usage of checkpointing mechanisms to save training states periodically. However, this can lead to a waste of training time and impact performance due to frequent I/O operations. Intel Optane Data Center Persistent Memory Module (DCPMM) is a novel memory device that combines non-volatility, high bandwidth, and low latency. Intuitively, deploying LLMs on DCPMM could enhance I/O performance, but the DCPMM cannot fully adapt to the current deep learning frameworks and the read/write granularity mismatch of DCPMM needs to be resolved. To this end, we propose PMCKPT, a checkpointing scheme customized for hybrid memory architectures (DRAM-PM). PMCKPT provides a storage engine to mitigate the impact of frequent I/O operations on training. Furthermore, PMCKPT ensures data persistence and consistency through a persistence scheme based on the persistent memory development kit (PMDK). Finally, it also incorporates a checkpointing management strategy to handle historical version checkpointing and optimize storage management. We implement PMCKPT in ChatGLM and the experimental results demonstrate that PMCKPT can effectively reduce training time by about 34% compared to traditional schemes which use SSDs. © 2024 IEEE.

关键词： Memory architecture

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather 31

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Seoul National University Department of Electrical and Computer Engineering Korea Republic of Old Dominion Univeristy School of Data Science United States Samsung Electronics Korea Republic of

ISBN: (纸本)9798331506476

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025 IEEE.

关键词： Memory architecture

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Tall-and-Skinny QR Factorization for Clusters of GPUs Using high-performance Building Blocks

Tall-and-Skinny QR Factorization for Clusters of GPUs Using ...

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29th international Conference on Parallel and Distributed computing (Euro-Par)

作者： Tomas, Andres E. Quintana-Orti, Enrique S. Univ Politecn Valencia Dept Informat Sistemas & Comp Valencia Spain Univ Valencia Dept Informat Valencia Spain

ISBN: (纸本)9783031506833;9783031506840

We build a message-passing realization of the QR factorization for tall-and-skinny matrices on top of highly parallel linear algebra kernels, such as various types of matrix multiplications and triangular system solves, plus a few small Cholesky decompositions. Our solution, using either the NVIDIA Collective Communications Library (NCCL) or a plain instance of MPI as the message-passing layer, and the implementation of these kernels in linear algebra libraries, can run both on clusters of multicore nodes, possibly accelerated with GPUs, or on multi-GPU platforms. The experimental evaluation of our parallel algorithm for the QR factorization on a cluster of 8 nodes with NVIDIA A100 boards shows significant acceleration factors over a code from MAGMA, based on House-lholder reflectors, that provides the same functionality. In addition, the experiments show a fair weak scalability when the problem has many more rows than columns.

关键词： QR factorization modified Gram-Schmidt method high performance computing graphics processing units (GPUs)

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FRAME 2024 - Proceedings of the 2024 on Workshop on Flexible Resource and Application Management on the Edge, Part of: HPDC 2024 - 3rd international symposium on high-performance Parallel and Distributed computing

FRAME 2024 - Proceedings of the 2024 on Workshop on Flexible...

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4th Workshop on Flexible Resource and Application Management on the Edge, FRAME 2024

ISBN: (纸本)9798400706417

The proceedings contain 9 papers. The topics discussed include: towards sustainable deployment of microservices over the Cloud-IoT continuum, with FREEDA;towards enabling observability of energy demand, with NEST;urgent edge computing;optimizing resource allocation in the edge: a minimum weighted vertex cover approach;towards energy-aware execution and offloading of serverless functions;reactive autoscaling of Kubernetes nodes;OUTFIT: crowdsourced data feeding noise maps in digital twins;encoding consistency: optimizing self-driving reliability with real-time speed data;and striking trade-off between high performance and energy efficiency in an edge computing application for detecting floating plastic debris.

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DroMPI: Parallel Computation Over Drop computing 24

DroMPI: Parallel Computation Over Drop Computing

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24th IEEE/ACM international symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Grosu, George-Mircea Nistor, Silvia-Elena Ciobanu, Radu-Ioan Dobre, Ciprian Pop, Florin Natl Univ Sci & Technol Politehn Bucharest Bucharest Romania Natl Inst Res & Dev Informat ICI Bucharest Romania Acad Romanian Scientists Bucharest Romania

ISBN: (纸本)9798350377521;9798350377514

With the advancement of technology and the spread of multi-core systems, the need for parallelization arises and the interest in programming models is growing. At the same time, new distributed computing models have been proposed, being in fierce competition to obtain the highest possible performance. The Drop computing Paradigm proposes the idea of decentralized computing over ad-hoc opportunistic networks of mobile and Edge devices. In this respect, the Drop computing model does not only aim to achieve a minimum turnaround time but also to optimize other characteristics related to mobile devices, such as limited resources and opportunistic communication. Therefore, it is necessary to define a new programming model called DroMPI that intends to extend the capabilities of current parallel and distributed programming models, based on the Drop computing paradigm. Therefore, the solution aims to develop a library that takes advantage of hardware capabilities in the interest of the Drop computing paradigm and also provides programmers with a high-level programming interface. The library's features will be based on the Message Passing Interface (MPI) standard, which will be responsible for inter-node parallelization. The name of the library, DroMPI, is an acronym for Drop computing and MPI. The implementation of the model will be responsible for the management of communication between nodes and for providing an Application Programming Interface (API) for the development of parallel applications in the Drop computing paradigm.

关键词： Decentralized computing Parallel Computation Drop computing Application Programming Interface MPI

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Energy-efficient online resource provisioning for cloud-edge platforms via multi-armed bandits 34

Energy-efficient online resource provisioning for cloud-edge...

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34th IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Rey-Jouanchicot, Jordan del Castillo, Juan Angel Lorenzo Zuckerman, Stephane Veronica Belmega, E. CY Cergy Paris Univ CY Tech Cergy France CY Cergy Paris Univ ETIS UMR 8051 ENSEA CNRS F-95000 Cergy France Univ Gustave Eiffel CNRS LIGM F-77454 Marne La Vallee France

ISBN: (数字)9781665451574

ISBN: (纸本)9781665451574

Edge computing is a new paradigm in which data are locally collected, aggregated and preprocessed before being sent to a Cloud platform. Edge devices, typically IoT objects, are characterized by limited computational capabilities, alongside high energy efficiency operations. In smart building or smart city applications, the overall amount of available IoT devices may account for an important computational capacity. Aggregating the idle CPU cycles of several devices would allow performing on-site parallel and distributed computing, while reusing the available resources in the network. Timely and important computational tasks such as video surveillance processing, HPC jobs, or deep neural network training, can be performed with local available resources. This includes local servers or a private, on-site cloud, as opposed to the use of public clouds, thus avoiding security issues and fostering energy efficiency. An ongoing research topic in such a distributed and volatile context is to design novel algorithms for resource provisioning that allow tasks to be distributed over a set of available IoT devices in an efficient manner. This work presents two online, adaptive and robust scheduling techniques, based on the UCB and EXP3 Multi-Armed Bandit algorithms (MABs), for resource management in Cloud-Edge computing based environments while improving performance and reducing energy consumption. The novelty of our adapted algorithms lies in the fact that we explicitly account for unavailable computing IoT devices resulting in a time-varying available set of arms at each stage. Our numerical results show the high relevance of our approach in reaching optimal provisioning policies in time-varying environments.

关键词： Cloud Edge computing resource provisioning online learning multi-armed bandits

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SWARM STORM: An Automated Chaos Tool for Docker Swarm Applications 24

SWARM STORM: An Automated Chaos Tool for Docker Swarm Applic...

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33rd international symposium on high-performance Parallel and Distributed computing (HPDC)

作者： Higgins, Travis Jha, Devki Nandan Ranjan, Rajiv Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798400704130

Docker Swarm facilitates the deployment of modular applications in an independent yet interconnected manner. Applications within the Swarm communicate through Docker's internal network, ensuring rapid computation and enhanced security. Despite these advantages, the complex nature of software development often leads to the occurrence of various faults, including memory leaks, application failures, and network outages. To systematically identify and address such issues, chaos engineering has emerged as a powerful approach within both development and production environments. However, conducting chaos experiments within a Docker Swarm cluster in a multi-cloud environment proves to be challenging. In this paper, we present Swarm Storm, an automated framework designed for orchestrating chaos engineering experiments within Docker Swarm-based clusters in a cloud-agnostic environment with comprehensive testing features. We validate Swarm Storm using a simple Java benchmark application, demonstrating its effectiveness in addressing faults within the system.

关键词： Docker Swarm Fault Chaos Engineering Cloud computing

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Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling

Scalable and Consistent Graph Neural Networks for Distribute...

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2024 workshops of the international Conference for high performance computing, Networking, Storage and Analysis, SC workshops 2024

作者： Barwey, Shivam Balin, Riccardo Lusch, Bethany Patel, Saumil Balakrishnan, Ramesh Pal, Pinaki Maulik, Romit Vishwanath, Venkatram Transportation and Power Systems Argonne National Laboratory United States Leadership Computing Facility Argonne National Laboratory United States Argonne National Laboratory Computational Science Division United States Pennsylvania State University Information Sciences and Technology United States Mathematics and Computer Science Argonne National Laboratory United States

ISBN: (纸本)9798350355543

This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer. © 2024 IEEE.

关键词： graph neural networks mesh-based modeling

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A Research-Based Course Module to Study Non-determinism in high performance Applications 36

A Research-Based Course Module to Study Non-determinism in H...

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36th IEEE international Parallel and Distributed Processing symposium (IEEE IPDPS)

作者： Bell, Patrick Suarez, Kae Fossum, Barbara Chapp, Dylan Bhowmick, Sanjukta Taufer, Michela Univ Tennessee Knoxville TN 37996 USA Univ North Texas Denton TX 76203 USA

ISBN: (纸本)9781665497473

We present a research-based course module to teach computer science students, software developers, and scientists the effects of non-determinism on high performance applications. The course module uses the ANACIN-X software package, a suite of software modules developed by the authors;ANACIN-X provides test cases, analytic tools to run different scenarios (e.g., using different numbers of processes and different communication patterns), and visualization tools for beginner, intermediate, and advanced level understandings in non-determinism. Through our course module, students in computer science, software developers, and scientists gain an understanding of non-determinism, how to measure its occurrence in an execution, and how to identify its root causes within an application's code.

关键词： high performance computing Parallelism Message Passing Workforce Development Debugging

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Designing a Sustainable Serverless Graph Processing Tool on the computing Continuum

Designing a Sustainable Serverless Graph Processing Tool on ...

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29th international Conference on Parallel and Distributed computing (Euro-Par)

作者： Farahani, Reza Ristov, Sashko Prodan, Radu Alpen Adria Univ Klagenfurt Klagenfurt Austria Univ Innsbruck Innsbruck Austria

ISBN: (纸本)9783031488023;9783031488030

Graph processing has become increasingly popular and essential for solving complex problems in various domains, like social networks. However, the processing of graphs on a massive scale poses critical challenges, such as inefficient utilization of resources and energy. To bridge such challenges, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. This paper presents an initial architectural design for the Graph-Choreographer, one of the five Graph-Massivizer tools. We explain Graph-Choreographer's components and demonstrate how Graph-Choreographer can adopt the emerging serverless computing paradigm to process Basic Graph Operations (BGOs) as serverless functions across the computing continuum efficiently. We also present an early vision of our federated Function-as-a-Service (FaaS) testbed, which will be used to conduct experiments and assess the tool's performance.

关键词： computing Continuum Serverless FaaS Massive Graph

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