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μManycore: A Cloud-Native CPU for Tail at Scale 23

μManycore: A Cloud-Native CPU for Tail at Scale

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50th Annual international symposium on computer architecture (ISCA)

作者： Stojkovic, Jovan Liu, Chunao Shahbaz, Muhammad Torrellas, Josep Univ Illinois Champaign IL 61820 USA Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798400700958

Microservices are emerging as a popular cloud-computing paradigm. Microservice environments execute typically-short service requests that interact with one another via remote procedure calls (often across machines), and are subject to stringent tail-latency constraints. In contrast, current processors are designed for traditional monolithic applications. They support global hardware cache coherence, provide large caches, incorporate microarchitecture for long-running, predictable applications (such as advanced prefetching), and are optimized to minimize average latency rather than tail latency. To address this imbalance, this paper proposes mu Manycore, an architecture optimized for cloud-native microservice environments. Based on a characterization of microservice applications, mu Manycore is designed to minimize unnecessary microarchitecture and mitigate overheads to reduce tail latency. Indeed, rather than supporting manycore-wide hardware cache coherence, mu Manycore has multiple small hardware cache-coherent domains, called Villages. Clusters of villages are interconnected with an on-package leaf-spine network, which has many redundant, low-hop-count paths between clusters. To minimize latency overheads, mu Manycore schedules and queues service requests in hardware, and includes hardware support to save and restore process state when doing a context-switch. Our simulation-based results show that mu Manycore delivers high performance. A cluster of 10 servers with a 1024-core mu Manycore in each server delivers 3.7x lower average latency, 15.5x higher throughput, and, importantly, 10.4x lower tail latency than a cluster with iso-power conventional server-class multicores. Similar good results are attained compared to a cluster with power-hungry iso-area conventional server-class multicores.

关键词： Microservices cloud computing manycore architecture

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Comparing the Orchestration of Quantum Applications on Hybrid Clouds 23

Comparing the Orchestration of Quantum Applications on Hybri...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Sangle, Rajiv Khare, Tuhin Seshadri, Padmanabha V. Simmhan, Yogesh Indian Inst Sci Bangalore 560012 Karnataka India IBM India Res Lab Bangalore Karnataka India

ISBN: (纸本)9798350302080

Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM's Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.

关键词： Hybrid Cloud Hybrid Quantum Classical (HQC) Qiskit Runtime Quantum computing

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Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems 36

Modeling Power Consumption of Lossy Compressed I/O for Exasc...

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

作者： Wilkins, Grant Calhoun, Jon C. Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (纸本)9781665497473

Exascale computing enables unprecedented, detailed and coupled scientific simulations which generate data on the order of tens of petabytes. Due to large data volumes, lossy compressors become indispensable as they enable better compression ratios and runtime performance than lossless compressors. Moreover, as (high-performance computing) HPC systems grow larger, they draw power on the scale of tens of megawatts. Data motion is expensive in time and energy. Therefore, optimizing compressor and data I/O power usage is an important step in reducing energy consumption to meet sustainable computing goals and stay within limited power budgets. In this paper, we explore efficient power consumption gains for the SZ and ZFP lossy compressors and data writing on a cloud HPC system while varying the CPU frequency, scientific data sets, and system architecture. Using this power consumption data, we construct a power model for lossy compression and present a tuning methodology that reduces energy overhead of lossy compressors and data writing on HPC systems by 14.3% on average. We apply our model and find 6.5 kJs, or 13%, of savings on average for 512GB DO. Therefore, utilizing our model results in more energy efficient lossy data compression and DO.

关键词： Power demand Runtime Exascale computing Computational modeling Systems architecture Writing Compressors

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PROCA: Programmable Probabilistic Processing Unit architecture with Accept/Reject Prediction & Multicore Pipelining for Causal Inference 31

PROCA: Programmable Probabilistic Processing Unit Architectu...

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

作者： Fu, Yihan Fan, Anjunyi Yue, Wenshuo Zhao, Hongxiao Shi, Daijing Wu, Qiuping Li, Jiayi Zhang, Xiangyu Tao, Yaoyu Yang, Yuchao Yan, Bonan Independent Researcher United States Peking University Beijing Advanced Innovation Center for Integrated Circuits School of Integrated Circuits Beijing China Peking University School of Electronic and Computer Engineering Shenzhen China Chinese Institute for Brain Research Beijing China

ISBN: (纸本)9798331506476

Causal inference is an important field in data science and cognitive artificial intelligence. It requires the construction of complex probabilistic models to describe the causal relationships between random variables. Probabilistic models rely on probabilistic programming as a flexible framework. However, the computing speed of probabilistic programming is often hindered by the extensive use of Markov chain Monte Carlo (MCMC) algorithms, even though they are powerful in Bayesian inference. To accelerate MCMC, this work presents PROCA, a programmable MCMC-based probabilistic processing unit architecture. PROCA exploits processing-in-memory function units to generate new samples of Markov chains. PROCA is programmable to execute the computation for arbitrary forms of posterior distribution formulas that software probabilistic programming frameworks support. We develop a novel accept/reject prediction methodology to accelerate the sequential MCMC computation, thereby introducing efficient multi-core pipelining methods. We implement and validate the PROCA architecture with commercial process development kits. The implementation is evaluated based on 9 representative benchmarks, covering PyMC official tutorial probabilistic problems, single-variable probabilistic problems, and real-world causal inference problems. Our comprehensive experiments demonstrate that PROCA achieves a speedup of 172~4871 × compared to Intel Xeon Gold CPU, 42 ∼ 1058 × compared to NVIDIA A100 GPU, and 1.765 × over state-of-the-art MCMC accelerators, respectively. PROCA achieves comparable statistical robustness to the software probabilistic programming frameworks. Compared with state-of-the-art MCMC domain-specific accelerators, our design boosts the energy efficiency by 9.47 ×. © 2025 IEEE.

关键词： Markov chains

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Quantum communications for image transmission over error-prone channels

Quantum communications for image transmission over error-pro...

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international symposium, Seminar, and Exhibition, Display Week 2024

作者： Jayasinghe, Udara Samarathunga, Prabhath Ganearachchi, Yasith Fernando, Thanuj Fernando, Anil Department of Computer and Information Sciences University of Strathclyde Glasgow United Kingdom

The introduction of quantum communications, enabled by advancements in quantum computing, is expected to play a significant role in the field of communications. Inherent properties of quantum objects, such as superposition and entanglement, have the potential to provide novel solutions to overcome the challenges encountered by classical communication systems in bandwidth-intensive applications such as media transmission. This research explores the performance of a quantum communication system in image transmission using quantum superposition and investigates its performance using a simple quantum channel model. With an increase in channel noise, there are significant gains in the rate distortion performance of images transmitted over the quantum channel compared to an ideal classical channel. This novel attempt at constructing a quantum communication-based image transmission system indicates the potential of the approach to be applied to satisfy the ever-increasing demands of high-quality media transmission applications. © 2024 The Author(s).

关键词： Quantum channel

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LAD: Efficient Accelerator for Generative Inference of LLM with Locality Aware Decoding 31

LAD: Efficient Accelerator for Generative Inference of LLM w...

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

作者： Wang, Haoran Li, Yuming Xu, Haobo Wang, Ying Liu, Liqi Yang, Jun Han, Yinhe Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9798331506476

Large Language Models (LLMs) have emerged as the cornerstone of content generation applications due to their ability to capture relations between newly generated token and the full preceding context. However, this ability stems from the attention mechanism for decoding that retains the entire generation history as key value cache (KV cache). As the generated sequence lengthens, the KV cache expands, causing a substantial memory access bottleneck. In advanced LLM generation systems running on GPUs, the attention mechanism for decoding accounts for more than 50% of the total inference time when the KV cache length reaches 4096. To address this issue, this paper introduces LAD (Locality Aware Decoding), an LLM generation accelerator with algorithm-hardware enhancements that significantly decrease KV cache access, resulting in considerable speedups and energy savings. A key insight underlying LAD is that when the attention score for a specific position remains fixed over the next several decoding steps, it is unnecessary to repeatedly retrieve the associated key and value at each step to reproduce the computation. Our analysis reveals that numerous positions exhibit notable numerical locality in attention scores through multiple decoding steps. Leveraging these insights, we have designed an innovative attention decoding computation method that decreases the frequency of accessing the key and value for positions demonstrating good locality, all while maintaining decoding accuracy. Extensive experiments show that LAD generates sequences with an average ROUGE-1 similarity of 97% compared to those generated by the original model. When the length of KV cache exceeds 2048, the high configuration of LAD accelerator achieves on average (geomean) 10.7 × speedup and 52.4 × energy efficiency for the attention mechanism compared to the A100 GPU. For end-to-end model inference, it also achieves on average 2.3 × speedup and 13.4 × energy efficiency. © 2025 IEEE.

关键词： Program processors

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Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Java

Benchmarking the Parallel 1D Heat Equation Solver in Chape...

引用

international workshops held at the 29th international Conference on Parallel and Distributed computing, Euro-Par 2023

作者： Diehl, Patrick Morris, Max Brandt, Steven R. Gupta, Nikunj Kaiser, Hartmut Center of Computation and Technology Louisiana State University Baton Rouge United States Department of Physics and Astronomy Louisiana State University Baton Rouge United States Department of Computer Science Louisiana State University Baton Rouge United States Seattle United States

ISBN: (纸本)9783031488023

Many scientific high performance codes that simulate e.g. black holes, coastal waves, climate and weather, etc. rely on block-structured meshes and use finite differencing methods to solve the appropriate systems of differential equations iteratively. This paper investigates implementations of a straightforward simulation of this type using various programming systems and languages. We focus on a shared memory, parallelized algorithm that simulates a 1D heat diffusion using asynchronous queues for the ghost zone exchange. We discuss the advantages of the various platforms and explore the performance of this model code on different computing architectures: Intel, AMD, and ARM64FX. As a result, Python was the slowest of the set we compared. Java, Go, Swift, and Julia were the intermediate performers. The higher performing platforms were C++, Rust, Chapel, Charm++, and HPX. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Nudge Me and I Change: The Effect of Digital Nudging on Software Engineers’ Motivation and Behaviour Towards Data Quality 37

Nudge Me and I Change: The Effect of Digital Nudging on Soft...

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37th international BCS Human-computer Interaction Conference, BCS HCI 2024

作者： Alkadhi, Bushra Hendley, Robert Bahsoon, Rami School of Computer Science University of Birmingham Birmingham United Kingdom

Maintaining high Data Quality (DQ) is critical in data-driven approaches;however, it is still an open challenge in Data-Driven Requirements Engineering (DDRE) where data steers the requirements engineering process. The software engineering view of DQ is still limited and is often neglected due to a lack of motivation and the inadequacy of skills in identifying DQ issues. Issues such as bias, inconsistency, noise etc. can compromise the quality of the analysis and product. This work draws upon work in behaviour change, in particular nudge theory, to motivate software engineers to adopt better practices towards DQ. We report on a preliminary controlled experiment, where we empirically evaluate the effect of digital nudging in terms of performance, efficiency, and engagement. Our positive findings provide promising initial empirical insights and pave the way for further research on the use of digital nudging for altering human behaviour in general, and for software engineers in particular. © Alkadhi et al.

关键词： Behaviour Change Data Quality Data-Driven Requirements Engineering Human-Centred computing Motivation Nudge Theory Software Engineering User Studies

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Intelligent elastic resource allocation strategy based on supervised learning 9

Intelligent elastic resource allocation strategy based on su...

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9th international symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Li, Lina Hu, Chuncheng Wang, Shuxin Lv, Junan Xu, Yiming Li, Nianfeng Changchun University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798350388404

In streaming computing systems, flexible resource allocation for time-varying data is the key to ensure application and system performance. Traditional resource allocation methods and existing intelligent methods have the problems of limited practicability and high training costs. In order to minimize application delay, this paper proposes an intelligent elastic resource allocation strategy based on supervised learning. The strategy uses the deep neural network as the strategy model and conducts model training through supervised learning. After obtaining the basic system status and application load, it automatically performs near-optimal application resource allocation. We conducted policy performance evaluation on three real datasets. The experimental results show that compared with reinforcement learning strategy, our strategy has better performance in terms of delay violation rate and throughput, and is close to the performance of the optimal strategy. © 2024 IEEE.

关键词： Self-supervised learning

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Research on Unsupervised Adaptive Semantic Segmentation based on 'Template' and 'Paint' Generative Model 5

Research on Unsupervised Adaptive Semantic Segmentation base...

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5th international symposium on computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Song, Yaodong School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Province Wuhan China

ISBN: (纸本)9798331518677

In unsupervised domain adaptive semantic segmentation, the lack of labeled data in the target domain is a common challenge. The traditional method generates a composite image in the style of the target domain by generating the joint distribution of the source domain and the target domain by generating the model, but the problems of poor sample quality and high training cost still exist. Therefore, this paper proposes a new generation strategy, which takes the label data in the source domain as a 'template' and the generated data as a 'paint', and colors different object categories in the label data in the target domain style, and generates image data with the target domain style and consistent with the source domain labels. This method can improve the performance of the segmentation model while maintaining the quality of the generated samples, and significantly reduce the consumption of training time and computing resources. Experimental results show that the proposed method has achieved significant improvement in the semantic segmentation task of the target domain, and shows good robustness and generalization ability. © 2024 IEEE.

关键词： Generative adversarial networks

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