Bo Wang, Anara Kozhokanova, C. Terboven, Matthias S. Müller
{"title":"RLP: Power Management Based on a Latency-Aware Roofline Model","authors":"Bo Wang, Anara Kozhokanova, C. Terboven, Matthias S. Müller","doi":"10.1109/IPDPS54959.2023.00052","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00052","url":null,"abstract":"The ever-growing power draw in high-performance computing (HPC) clusters and the rising energy costs enforce a pressing urge for energy-efficient computing. Consequently, advanced infrastructure orchestration is required to regulate power dissipation efficiently. In this work, we propose a novel approach for managing power consumption at runtime based on the well-known roofline model and call it Roofline Power (RLP) management. The RLP employs rigorously selected but generally available hardware performance events to construct rooflines, with minimal overheads. In particular, RLP extends the original roofline model to include the memory access latency metric for the first time. The extension identifies whether execution is bandwidth, latency, or compute-bound, and improves the modeling accuracy. We evaluated the RLP model on server-grade CPUs and a GPU with real-world HPC workloads in two scenarios: optimization with and without power capping. Compared to system default settings, RLP reduces the energy-to-solution up to 22% with negligible performance degradation. The other scenario accelerates the execution up to 14.7% under power capping. In addition, RLP outperforms other state-of-the-art techniques in generality and effectiveness.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128681438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from the IPDPS 2023 General Co-chairs","authors":"","doi":"10.1109/ipdps54959.2023.00005","DOIUrl":"https://doi.org/10.1109/ipdps54959.2023.00005","url":null,"abstract":"","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117180757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud","authors":"Tianyao Shi, Yingxuan Yang, Yunlong Cheng, Xiaofeng Gao, Zhen Fang, Yongqiang Yang","doi":"10.1109/IPDPS54959.2023.00095","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00095","url":null,"abstract":"Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters.We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth’s interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127097704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proactive SLA-aware Application Placement in the Computing Continuum","authors":"Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, R.-C. Prodan","doi":"10.1109/IPDPS54959.2023.00054","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00054","url":null,"abstract":"The accelerating growth of modern distributed applications with low delivery deadlines leads to a paradigm shift towards the multi-tier computing continuum. However, the geographical dispersion, heterogeneity, and availability of the continuum resources may result in failures and quality of service degradation, significantly negating its advantages and lowering users’ satisfaction. We propose in this paper a proactive application placement (PROS) method relying on distributed coordination to prevent the quality of service violations through service-level agreements on the computing continuum. PROS employs a sigmoid function with adaptive weights for the different parameters to predict the service level agreement assurance of devices based on their past credentials and current capabilities. We evaluate PROS using two application workloads with different traffic stress levels up to 90 million services on a real testbed with 600 heterogeneous instances deployed over eight geographical locations. The results show that PROS increases the success rate by 7%–33%, reduces the response time by 16%–38%, and increases the deadline satisfaction rate by 19%–42% compared to two related work methods. A comprehensive simulation study with 1000 devices and a workload of up to 670 million services confirm the scalability of the results.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126744447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael J. Brim, A. Moody, Seung-Hwan Lim, Ross G. Miller, Swen Boehm, Cameron Stanavige, K. Mohror, S. Oral
{"title":"UnifyFS: A User-level Shared File System for Unified Access to Distributed Local Storage","authors":"Michael J. Brim, A. Moody, Seung-Hwan Lim, Ross G. Miller, Swen Boehm, Cameron Stanavige, K. Mohror, S. Oral","doi":"10.1109/IPDPS54959.2023.00037","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00037","url":null,"abstract":"We introduce UnifyFS, a user-level file system that aggregates node-local storage tiers available on high performance computing (HPC) systems and makes them available to HPC applications under a unified namespace. UnifyFS employs transparent I/O interception, so it does not require changes to application code and is compatible with commonly used HPC I/O libraries. The design of UnifyFS supports the predominant HPC I/O workloads and is optimized for bulk-synchronous I/O patterns. Furthermore, UnifyFS provides customizable file system semantics to flexibly adapt its behavior for diverse I/O workloads and storage devices. In this paper, we discuss the unique design goals and architecture of UnifyFS and evaluate its performance on a leadership-class HPC system. In our experimental results, we demonstrate that UnifyFS exhibits excellent scaling performance for write operations and can improve the performance of application checkpoint operations by as much as 3× versus a tuned configuration.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117295350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism","authors":"Zhenghang Zhang, Donglin Yang, Yaqi Xia, Liang Ding, Dacheng Tao, Xiaobo Zhou, Dazhao Cheng","doi":"10.1109/IPDPS54959.2023.00026","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00026","url":null,"abstract":"Recently, Mixture-of-Experts (MoE) has become one of the most popular techniques to scale pre-trained models to extraordinarily large sizes. Dynamic activation of experts allows for conditional computation, increasing the number of parameters of neural networks, which is critical for absorbing the vast amounts of knowledge available in many deep learning areas. However, despite the existing system and algorithm optimizations, there are significant challenges to be tackled when it comes to the inefficiencies of communication and memory consumption.In this paper, we present the design and implementation of MPipeMoE, a high-performance library that accelerates MoE training with adaptive and memory-efficient pipeline parallelism. Inspired by that the MoE training procedure can be divided into multiple independent sub-stages, we design adaptive pipeline parallelism with an online algorithm to configure the granularity of the pipelining. Further, we analyze the memory footprint breakdown of MoE training and identify that activations and temporary buffers are the primary contributors to the overall memory footprint. Toward memory efficiency, we propose memory reusing strategies to reduce memory requirements by eliminating memory redundancies, and develop an adaptive selection component to determine the optimal strategy that considers both hardware capacities and model characteristics at runtime. We implement MPipeMoE upon PyTorch and evaluate it with common MoE models in a physical cluster consisting of 8 NVIDIA DGX A100 servers. Compared with the state-of-art approach, MPipeMoE achieves up to 2.8× speedup and reduces memory footprint by up to 47% in training large models.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132710551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed W. Hassan, A. Dabah, H. Ltaief, Suhaib A. Fahmy
{"title":"Signal Detection for Large MIMO Systems Using Sphere Decoding on FPGAs","authors":"Mohamed W. Hassan, A. Dabah, H. Ltaief, Suhaib A. Fahmy","doi":"10.1109/IPDPS54959.2023.00020","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00020","url":null,"abstract":"Wireless communication systems rely on aggressive spatial multiplexing Multiple-Input Multiple-Output (MIMO) access points to enhance network throughput. A significant computational hurdle for large MIMO systems is signal detection and decoding, which has exponentially increasing computational complexity as the number of antennas increases. Hence, the feasibility of large MIMO systems depends on suitable implementations of signal decoding schemes.This paper presents an FPGA-based Sphere Decoder (SD) architecture that provides high-performance signal decoding for large MIMO systems, supporting up to 16-QAM modulation. The SD algorithm is refactored to map well to the FPGA architecture using a GEMM-based approach to exploit the parallel computational power of FPGAs. We implement FPGA-specific optimization techniques to improve computational complexity. We show significant improvement in time to decode the received signal with under 10–2 BER. The design is deployed on a Xilinx Alveo U280 FPGA and shows up to a 9× speedup compared to optimized multi-core CPU execution, achieving real-time requirements. Our proposed design reduces power consumption by a geo-mean of 38.1× compared to CPU implementation, which is important in real-world deployments. We also evaluate our design against alternative approaches on GPU.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131330761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SelB-k-NN: A Mini-Batch K-Nearest Neighbors Algorithm on AI Processors","authors":"Yifeng Tang, Cho-Li Wang","doi":"10.1109/IPDPS54959.2023.00088","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00088","url":null,"abstract":"The popularity of Artificial Intelligence (AI) motivates novel domain-specific hardware named AI processors. With a design trade-off, the AI processors feature incredible computation power for matrix multiplications and activations, while some leave other operations less powerful, e.g., scalar operations and vectorized comparisons & selections. For k-nearest neighbors (k-NN) algorithm, consisting of distance computation phase and k-selection phase, while the former is naturally accelerated, the previous efficient k-selection becomes problematic. Moreover, limited memory forces k-NN to adopt a mini-batch manner with tiling technique. As the distance computation’s results are the k-selection’s inputs, the former’s tiling shape determines that of the latter. Since the two phases execute on separate hardware units requiring different performance analyses, whether the former’s tiling strategies benefit the latter and entire k-NN is doubtful.To address the new challenges brought by the AI processors, this paper proposes SelB-k-NN (Selection-Bitonic-k-NN), a mini-batch algorithm inspired by selection sort and bitonic k-selection. SelB-k-NN avoids the expansion of the weakly-supported operations on the huge scale of datasets. To apply SelB-k-NN to various AI processors, we propose two algorithms to reduce the hardware support requirements. Since the matrix multiplication operates data with the specifically-designed memory hierarchy which k-selection does not share, the tiling shape of the former cannot guarantee the best execution of the latter and vice versa. By quantifying the runtime workload variations of k-selection, we formulate an optimization problem to search for the optimal tiling shapes of both phases with an offline pruning method, which reduces the search space in the preprocessing stage. Evaluations show that on Huawei Ascend 310 AI processor, SelB-k-NN achieves 2.01× speedup of the bitonic k-selection, 23.93× of the heap approach, 78.52× of the CPU approach. For mini-batch SelB-k-NN, the optimal tiling shapes for two phases respectively achieve 1.48× acceleration compared with the matrix multiplication tiling shapes and 1.14× with the k-selection tiling shapes, with 72.80% of the search space pruned.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117286368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haodi Lu, Haikun Liu, Chencheng Ye, Xiaofei Liao, Fubing Mao, Yu Zhang, Hai Jin
{"title":"Software-Defined, Fast and Strongly-Consistent Data Replication for RDMA-Based PM Datastores","authors":"Haodi Lu, Haikun Liu, Chencheng Ye, Xiaofei Liao, Fubing Mao, Yu Zhang, Hai Jin","doi":"10.1109/IPDPS54959.2023.00019","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00019","url":null,"abstract":"Modern storage systems typically replicate data on multiple servers to provide high reliability and availability. However, most commercially-deployed datastores often fail to offer low latency, high throughput, and strong consistency at the same time. This paper presents Whale, a Remote Direct Memory Access (RDMA) based primary-backup replication system for in-memory datastores. Whale achieves both low latency and strong consistency by decoupling metadata multicasting from data replication for all backup nodes, and using an optimistic commitment mechanism to respond to client write requests earlier. Whale achieves high throughput by propagating writes from the primary node to backup nodes asynchronously via RDMA-optimized chain replication. To further reduce the cost of data replication, we design a log-structured datastore to fully exploit the advantages of one-sided RDMA and Persistent Memory (PM). We implement Whale on a cluster equipped with PM and InfiniBand RDMA networks. Experimental results show that Whale achieves much higher throughput and lower latency than state-of-the-art replication protocols.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}