Pub Date : 2023-05-01DOI: 10.1109/IPDPS54959.2023.00052
Bo Wang, Anara Kozhokanova, C. Terboven, Matthias S. Müller
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.
{"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}
Pub Date : 2023-05-01DOI: 10.1109/ipdps54959.2023.00005
{"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}
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.
{"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}
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.
{"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}
Pub Date : 2023-05-01DOI: 10.1109/IPDPS54959.2023.00037
Michael J. Brim, A. Moody, Seung-Hwan Lim, Ross G. Miller, Swen Boehm, Cameron Stanavige, K. Mohror, S. Oral
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.
{"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}
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.
{"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}
Pub Date : 2023-05-01DOI: 10.1109/IPDPS54959.2023.00020
Mohamed W. Hassan, A. Dabah, H. Ltaief, Suhaib A. Fahmy
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.
{"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}
Pub Date : 2023-05-01DOI: 10.1109/IPDPS54959.2023.00051
Lanshun Nie, Yuqi Qiu, Fei Meng, Mo Yu, Jing Li
Resource allocation for stream processing graphs on computing devices is critical to the performance of stream processing. Efficient allocations need to balance workload distribution and minimize communication simultaneously and globally. Since this problem is known to be NP-complete, recent machine learning solutions were proposed based on an encoder-decoder framework, which predicts the device assignment of computing nodes sequentially as an approximation. However, for large graphs, these solutions suffer from the deficiency in handling long-distance dependency and global information, resulting in suboptimal predictions. This work proposes a new paradigm to deal with this challenge, which first coarsens the graph and conducts assignments on the smaller graph with existing graph partitioning methods. Unlike existing graph coarsening works, we leverage the theoretical insights in this resource allocation problem, formulate the coarsening of stream graphs as edge-collapsing predictions, and propose an edge-aware coarsening model. Extensive experiments on various datasets show that our framework significantly improves over existing learning-based and heuristic-based baselines with up to 56% relative improvement on large graphs.
{"title":"Generalizable Reinforcement Learning-Based Coarsening Model for Resource Allocation over Large and Diverse Stream Processing Graphs","authors":"Lanshun Nie, Yuqi Qiu, Fei Meng, Mo Yu, Jing Li","doi":"10.1109/IPDPS54959.2023.00051","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00051","url":null,"abstract":"Resource allocation for stream processing graphs on computing devices is critical to the performance of stream processing. Efficient allocations need to balance workload distribution and minimize communication simultaneously and globally. Since this problem is known to be NP-complete, recent machine learning solutions were proposed based on an encoder-decoder framework, which predicts the device assignment of computing nodes sequentially as an approximation. However, for large graphs, these solutions suffer from the deficiency in handling long-distance dependency and global information, resulting in suboptimal predictions. This work proposes a new paradigm to deal with this challenge, which first coarsens the graph and conducts assignments on the smaller graph with existing graph partitioning methods. Unlike existing graph coarsening works, we leverage the theoretical insights in this resource allocation problem, formulate the coarsening of stream graphs as edge-collapsing predictions, and propose an edge-aware coarsening model. Extensive experiments on various datasets show that our framework significantly improves over existing learning-based and heuristic-based baselines with up to 56% relative improvement on large graphs.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133003","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}
Pub Date : 2023-05-01DOI: 10.1109/IPDPS54959.2023.00098
Deepal Tennakoon, Yiding Hua, V. Gramoli
Decentralization promises to remedy the drawbacks of the web by executing decentralized applications (DApps) on blockchains. Unfortunately, modern blockchains cannot support realistic web application workloads mainly due to congestion.We introduce the Smart Redbelly Blockchain (SRBB), a provably correct permissionless blockchain that reduces congestion by (1) avoiding redundant propagation and validations of transactions with Transaction Validation and Propagation Reduction (TVPR) and (2) mitigating the propagation of invalid transactions within blocks by Byzantine nodes with a dedicated Reward-Penalty Mechanism (RPM). Our comparison of SRBB against Algorand, Avalanche, Diem, Ethereum, Quorum, and Solana, using the DIABLO benchmark suite, indicates that SRBB outperforms all these blockchains under real application workloads. Moreover, SRBB is the only blockchain to successfully execute real workloads of NASDAQ and Uber on a DApp without losing transactions. To demonstrate that TVPR and RPM are the causes of the improved performance, we compare SRBB with its naive baseline, which does not contain TVPR and RPM. Our results show that TVPR increases the throughput by 55× and divides the latency by 3.5, while RPM increases the throughput by 7% under flooding attacks. Finally, TVPR helps reduce transaction losses in the normal scenario while RPM goes further and mitigates transaction losses under flooding attacks.
{"title":"Smart Redbelly Blockchain: Reducing Congestion for Web3","authors":"Deepal Tennakoon, Yiding Hua, V. Gramoli","doi":"10.1109/IPDPS54959.2023.00098","DOIUrl":"https://doi.org/10.1109/IPDPS54959.2023.00098","url":null,"abstract":"Decentralization promises to remedy the drawbacks of the web by executing decentralized applications (DApps) on blockchains. Unfortunately, modern blockchains cannot support realistic web application workloads mainly due to congestion.We introduce the Smart Redbelly Blockchain (SRBB), a provably correct permissionless blockchain that reduces congestion by (1) avoiding redundant propagation and validations of transactions with Transaction Validation and Propagation Reduction (TVPR) and (2) mitigating the propagation of invalid transactions within blocks by Byzantine nodes with a dedicated Reward-Penalty Mechanism (RPM). Our comparison of SRBB against Algorand, Avalanche, Diem, Ethereum, Quorum, and Solana, using the DIABLO benchmark suite, indicates that SRBB outperforms all these blockchains under real application workloads. Moreover, SRBB is the only blockchain to successfully execute real workloads of NASDAQ and Uber on a DApp without losing transactions. To demonstrate that TVPR and RPM are the causes of the improved performance, we compare SRBB with its naive baseline, which does not contain TVPR and RPM. Our results show that TVPR increases the throughput by 55× and divides the latency by 3.5, while RPM increases the throughput by 7% under flooding attacks. Finally, TVPR helps reduce transaction losses in the normal scenario while RPM goes further and mitigates transaction losses under flooding attacks.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"13 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":"114188769","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}
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