Pub Date : 2020-11-01DOI: 10.1109/SC41405.2020.00005
H. Yashiro, K. Terasaki, Yuta Kawai, Shuhei Kudo, T. Miyoshi, Toshiyuki Imamura, K. Minami, Hikaru Inoue, T. Nishiki, Takayuki Saji, M. Satoh, H. Tomita
Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was $sim$4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.
{"title":"A 1024-Member Ensemble Data Assimilation with 3.5-Km Mesh Global Weather Simulations","authors":"H. Yashiro, K. Terasaki, Yuta Kawai, Shuhei Kudo, T. Miyoshi, Toshiyuki Imamura, K. Minami, Hikaru Inoue, T. Nishiki, Takayuki Saji, M. Satoh, H. Tomita","doi":"10.1109/SC41405.2020.00005","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00005","url":null,"abstract":"Numerical weather prediction (NWP) supports our daily lives. Weather models require higher spatiotemporal resolutions to prepare for extreme weather disasters and reduce the uncertainty of predictions. The accuracy of the initial state of the weather simulation is also critical; thus, we need more advanced data assimilation (DA) technology. By combining resolution and ensemble size, we have achieved the world’s largest weather DA experiment using a global cloud-resolving model and an ensemble Kalman filter method. The number of grid points was $sim$4.4 trillion, and 1.3 PiB of data was passed from the model simulation part to the DA part. We adopted a data-centric application design and approximate computing to speed up the overall system of DA. Our DA system, named NICAM-LETKF, scales to 131,072 nodes (6,291,456 cores) of the supercomputer Fugaku with a sustained performance of 29 PFLOPS and 79 PFLOPS for the simulation and DA parts, respectively.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130926333","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00045
G. Ostrouchov, Don E. Maxwell, R. Ashraf, C. Engelmann, M. Shankar, James H. Rogers
The Cray XK7 Titan was the top supercomputer system in the world for a long time and remained critically important throughout its nearly seven year life. It was an interesting machine from a reliability viewpoint as most of its power came from 18,688 GPUs whose operation was forced to execute three rework cycles, two on the GPU mechanical assembly and one on the GPU circuitboards. We write about the last rework cycle and a reliability analysis of over 100,000 years of GPU lifetimes during Titan’s 6-year-long productive period. Using time between failures analysis and statistical survival analysis techniques, we find that GPU reliability is dependent on heat dissipation to an extent that strongly correlates with detailed nuances of the cooling architecture and job scheduling. We describe the history, data collection, cleaning, and analysis and give recommendations for future supercomputing systems. We make the data and our analysis codes publicly available.
{"title":"GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability","authors":"G. Ostrouchov, Don E. Maxwell, R. Ashraf, C. Engelmann, M. Shankar, James H. Rogers","doi":"10.1109/SC41405.2020.00045","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00045","url":null,"abstract":"The Cray XK7 Titan was the top supercomputer system in the world for a long time and remained critically important throughout its nearly seven year life. It was an interesting machine from a reliability viewpoint as most of its power came from 18,688 GPUs whose operation was forced to execute three rework cycles, two on the GPU mechanical assembly and one on the GPU circuitboards. We write about the last rework cycle and a reliability analysis of over 100,000 years of GPU lifetimes during Titan’s 6-year-long productive period. Using time between failures analysis and statistical survival analysis techniques, we find that GPU reliability is dependent on heat dissipation to an extent that strongly correlates with detailed nuances of the cooling architecture and job scheduling. We describe the history, data collection, cleaning, and analysis and give recommendations for future supercomputing systems. We make the data and our analysis codes publicly available.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691663","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}
Noisy Intermediate-Scale Quantum (NISQ) computers are being increasingly used for executing early-stage quantum programs to establish the practical realizability of existing quantum algorithms. These quantum programs have uses cases in the realm of high-performance computing ranging from molecular chemistry and physics simulations to addressing NP-complete optimization problems. However, NISQ devices are prone to multiple types of errors, which affect the fidelity and reproducibility of the program execution. As the technology is still primitive, our understanding of these quantum machines and their error characteristics is limited. To bridge that understanding gap, this is the first work to provide a systematic and rich experimental evaluation of IBM Quantum Experience (QX) quantum computers of different scales and topologies. Our experimental evaluation uncovers multiple important and interesting aspects of benchmarking and evaluating quantum program on NISQ machines. We have open-sourced our experimental framework and dataset to help accelerate the evaluation of quantum computing systems.
{"title":"Experimental Evaluation of NISQ Quantum Computers: Error Measurement, Characterization, and Implications","authors":"Tirthak Patel, Abhay Potharaju, Baolin Li, Rohan Basu Roy, Devesh Tiwari","doi":"10.1109/SC41405.2020.00050","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00050","url":null,"abstract":"Noisy Intermediate-Scale Quantum (NISQ) computers are being increasingly used for executing early-stage quantum programs to establish the practical realizability of existing quantum algorithms. These quantum programs have uses cases in the realm of high-performance computing ranging from molecular chemistry and physics simulations to addressing NP-complete optimization problems. However, NISQ devices are prone to multiple types of errors, which affect the fidelity and reproducibility of the program execution. As the technology is still primitive, our understanding of these quantum machines and their error characteristics is limited. To bridge that understanding gap, this is the first work to provide a systematic and rich experimental evaluation of IBM Quantum Experience (QX) quantum computers of different scales and topologies. Our experimental evaluation uncovers multiple important and interesting aspects of benchmarking and evaluating quantum program on NISQ machines. We have open-sourced our experimental framework and dataset to help accelerate the evaluation of quantum computing systems.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123804774","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00015
Zhenbo Qiao, Qing Liu, N. Podhorszki, S. Klasky, Jieyang Chen
As high-performance computing (HPC) is being scaled up to exascale to accommodate new modeling and simulation needs, I/O has continued to be a major bottleneck in the end-to-end scientific processes. Nevertheless, prior work in this area mostly aimed to maximize the average performance, and there has been a lack of study and solutions that can manage I/O performance variation on HPC systems. This work aims to take advantage of the storage characteristics and explore application level solutions that are interference-aware. In particular, we monitor the performance of data analytics and estimate the state of shared storage resources using discrete fourier transform (DFT). If heavy I/O interference is predicted to occur at a given timestep, data analytics can dynamically adapt to the environment by lowering the accuracy and performing partial or no augmentation from the shared storage, dictated by an augmentation-bandwidth plot. We evaluate three data analytics, XGC, GenASiS, and Jet, on Chameleon, and quantitatively demonstrate that both the average and variation of I/O performance can be vastly improved using our dynamic augmentation, with the mean and variance improved by as much as 67% and 96%, respectively, while maintaining acceptable outcome of data analysis.
{"title":"Taming I/O Variation on QoS-Less HPC Storage: What Can Applications Do?","authors":"Zhenbo Qiao, Qing Liu, N. Podhorszki, S. Klasky, Jieyang Chen","doi":"10.1109/SC41405.2020.00015","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00015","url":null,"abstract":"As high-performance computing (HPC) is being scaled up to exascale to accommodate new modeling and simulation needs, I/O has continued to be a major bottleneck in the end-to-end scientific processes. Nevertheless, prior work in this area mostly aimed to maximize the average performance, and there has been a lack of study and solutions that can manage I/O performance variation on HPC systems. This work aims to take advantage of the storage characteristics and explore application level solutions that are interference-aware. In particular, we monitor the performance of data analytics and estimate the state of shared storage resources using discrete fourier transform (DFT). If heavy I/O interference is predicted to occur at a given timestep, data analytics can dynamically adapt to the environment by lowering the accuracy and performing partial or no augmentation from the shared storage, dictated by an augmentation-bandwidth plot. We evaluate three data analytics, XGC, GenASiS, and Jet, on Chameleon, and quantitatively demonstrate that both the average and variation of I/O performance can be vastly improved using our dynamic augmentation, with the mean and variance improved by as much as 67% and 96%, respectively, while maintaining acceptable outcome of data analysis.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130706059","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00040
Kaiming Ouyang, Min Si, A. Hori, Zizhong Chen, P. Balaji
Load balance is essential for high-performance applications. Unbalanced communication can cause severe performance degradation, even in computation-balanced BSP applications. Designing communication-balanced applications is challenging, however, because of the diverse communication implementations at the underlying runtime system. In this paper, we address this challenge through an interprocess workstealing scheme based on process-memory-sharing techniques. We present CAB-MPI, an MPI implementation that can identify idle processes inside MPI and use these idle resources to dynamically balance communication workload on the node. We design throughput-optimized strategies to ensure efficient stealing of the data movement tasks. We demonstrate the benefit of work stealing through several internal processes in MPI, including intranode data transfer, pack/unpack for noncontiguous communication, and computation in one-sided accumulates. The implementation is evaluated through a set of microbenchmarks and proxy applications on Intel Xeon and Xeon Phi platforms.
{"title":"CAB-MPI: Exploring Interprocess Work-Stealing towards Balanced MPI Communication","authors":"Kaiming Ouyang, Min Si, A. Hori, Zizhong Chen, P. Balaji","doi":"10.1109/SC41405.2020.00040","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00040","url":null,"abstract":"Load balance is essential for high-performance applications. Unbalanced communication can cause severe performance degradation, even in computation-balanced BSP applications. Designing communication-balanced applications is challenging, however, because of the diverse communication implementations at the underlying runtime system. In this paper, we address this challenge through an interprocess workstealing scheme based on process-memory-sharing techniques. We present CAB-MPI, an MPI implementation that can identify idle processes inside MPI and use these idle resources to dynamically balance communication workload on the node. We design throughput-optimized strategies to ensure efficient stealing of the data movement tasks. We demonstrate the benefit of work stealing through several internal processes in MPI, including intranode data transfer, pack/unpack for noncontiguous communication, and computation in one-sided accumulates. The implementation is evaluated through a set of microbenchmarks and proxy applications on Intel Xeon and Xeon Phi platforms.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219324","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00080
Luc Jaulmes, Miquel Moretó, M. Valero, M. Erez, Marc Casas
Diminishing reliability of semiconductor technologies and decreasing power budgets per component hinder designing next-generation high performance computing (HPC) systems. Both constraints strongly impact memory subsystems, as DRAM main memory accounts for up to 30 to 50 percent of a node’s overall power consumption, and is the subsystem that is most subject to faults. Improving reliability requires stronger error correcting codes (ECCs), which incur additional power and storage costs. It is critical to develop strategies to uphold memory reliability while minimising these costs, with the goal of improving the power efficiency of computing machines.We introduce a methodology to dynamically estimate the vulnerability of data, and adjust ECC protection accordingly. Our methodology relies on information readily available to runtime systems in task-based dataflow programming models, and the existing Virtualized Error Correcting Code (VECC) schemes to provide adaptable protection. Guiding VECC using vulnerability estimates offers a wide range of reliabilityredundancy trade-offs, as reliable as using expensive offline profiling for guidance and up to to 25% safer than VECC without guidance. Runtime-guided VECC is more efficient than a stronger uniform ECC, reducing DIMM lifetime failure from 1.84% down to 1.26% while increasing DRAM energy consumption by only 1.03×.
{"title":"Runtime-Guided ECC Protection using Online Estimation of Memory Vulnerability","authors":"Luc Jaulmes, Miquel Moretó, M. Valero, M. Erez, Marc Casas","doi":"10.1109/SC41405.2020.00080","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00080","url":null,"abstract":"Diminishing reliability of semiconductor technologies and decreasing power budgets per component hinder designing next-generation high performance computing (HPC) systems. Both constraints strongly impact memory subsystems, as DRAM main memory accounts for up to 30 to 50 percent of a node’s overall power consumption, and is the subsystem that is most subject to faults. Improving reliability requires stronger error correcting codes (ECCs), which incur additional power and storage costs. It is critical to develop strategies to uphold memory reliability while minimising these costs, with the goal of improving the power efficiency of computing machines.We introduce a methodology to dynamically estimate the vulnerability of data, and adjust ECC protection accordingly. Our methodology relies on information readily available to runtime systems in task-based dataflow programming models, and the existing Virtualized Error Correcting Code (VECC) schemes to provide adaptable protection. Guiding VECC using vulnerability estimates offers a wide range of reliabilityredundancy trade-offs, as reliable as using expensive offline profiling for guidance and up to to 25% safer than VECC without guidance. Runtime-guided VECC is more efficient than a stronger uniform ECC, reducing DIMM lifetime failure from 1.84% down to 1.26% while increasing DRAM energy consumption by only 1.03×.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124234985","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00008
M. D. Ben, Charlene Yang, Zhenglu Li, F. Jornada, S. Louie, J. Deslippe
Large-scale GW calculations are the state-of-the-art approach to accurately describe many-body excited-state phenomena in complex materials. This is critical for novel device design but due to their extremely high computational cost, these calculations often run at a limited scale. In this paper, we present algorithm and implementation advancements made in the materials science code BerkeleyGW to scale calculations to the order of over 10,000 electrons utilizing the entire Summit at OLCF. Excellent strong and weak scaling is observed, and a 105.9 PFLOP/s double-precision performance is achieved on 27,648 V100 GPUs, reaching 52.7% of the peak. This work for the first time demonstrates the possibility to perform GW calculations at such scale within minutes on current HPC systems, and leads the way for future efficient HPC software development in materials, physical, chemical, and engineering sciences.
{"title":"Accelerating Large-Scale Excited-State GW Calculations on Leadership HPC Systems","authors":"M. D. Ben, Charlene Yang, Zhenglu Li, F. Jornada, S. Louie, J. Deslippe","doi":"10.1109/SC41405.2020.00008","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00008","url":null,"abstract":"Large-scale GW calculations are the state-of-the-art approach to accurately describe many-body excited-state phenomena in complex materials. This is critical for novel device design but due to their extremely high computational cost, these calculations often run at a limited scale. In this paper, we present algorithm and implementation advancements made in the materials science code BerkeleyGW to scale calculations to the order of over 10,000 electrons utilizing the entire Summit at OLCF. Excellent strong and weak scaling is observed, and a 105.9 PFLOP/s double-precision performance is achieved on 27,648 V100 GPUs, reaching 52.7% of the peak. This work for the first time demonstrates the possibility to perform GW calculations at such scale within minutes on current HPC systems, and leads the way for future efficient HPC software development in materials, physical, chemical, and engineering sciences.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122918288","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00054
Hugo Brunie, Costin Iancu, K. Ibrahim, P. Brisk, B. Cook
We present a methodology for precision tuning of full applications. These techniques must select a search space composed of either variables or instructions and provide a scalable search strategy. In full application settings one cannot assume compiler support for practical reasons. Thus, an additional important challenge is enabling code refactoring. We argue for an instruction-based search space and we show: 1) how to exploit dynamic program information based on call stacks; and 2) how to exploit the iterative nature of scientific codes, combined with temporal locality. We applied the methodology to tune the implementation of scientific codes written in a combination of Python, CUDA, C++ and Fortran, tuning calls to math exp library functions. The iterative search refinement always reduces the search complexity and the number of steps to solution. Dynamic program information increases search efficacy. Using this approach, we obtain application runtime performance improvements up to 27%.
{"title":"Tuning Floating-Point Precision Using Dynamic Program Information and Temporal Locality","authors":"Hugo Brunie, Costin Iancu, K. Ibrahim, P. Brisk, B. Cook","doi":"10.1109/SC41405.2020.00054","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00054","url":null,"abstract":"We present a methodology for precision tuning of full applications. These techniques must select a search space composed of either variables or instructions and provide a scalable search strategy. In full application settings one cannot assume compiler support for practical reasons. Thus, an additional important challenge is enabling code refactoring. We argue for an instruction-based search space and we show: 1) how to exploit dynamic program information based on call stacks; and 2) how to exploit the iterative nature of scientific codes, combined with temporal locality. We applied the methodology to tune the implementation of scientific codes written in a combination of Python, CUDA, C++ and Fortran, tuning calls to math exp library functions. The iterative search refinement always reduces the search complexity and the number of steps to solution. Dynamic program information increases search efficacy. Using this approach, we obtain application runtime performance improvements up to 27%.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124735584","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00068
Amirhessam Yazdi, Xing Lin, Lei Yang, Feng Yan
With the rapid growth in scale and complexity, today’s enterprise storage systems need to deal with significant amounts of errors. Existing proactive methods mainly focus on machine learning techniques trained using SMART measurements. However, such methods are usually expensive to use in practice and can only be applied to a limited types of errors with a limited scale. We collected more than 23-million storage events from 87 deployed NetApp-ONTAP systems managing 14,371 disks for two years and propose a lightweight training-free storage error forecasting method SEFEE. SEFEE employs Tensor Decomposition to directly analyze storage error-event logs and perform online error prediction for all error types in all storage nodes. SEFEE explores hidden spatio-temporal information that is deeply embedded in the global scale of storage systems to achieve record breaking error forecasting accuracy with minimal prediction overhead.
{"title":"SEFEE: Lightweight Storage Error Forecasting in Large-Scale Enterprise Storage Systems","authors":"Amirhessam Yazdi, Xing Lin, Lei Yang, Feng Yan","doi":"10.1109/SC41405.2020.00068","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00068","url":null,"abstract":"With the rapid growth in scale and complexity, today’s enterprise storage systems need to deal with significant amounts of errors. Existing proactive methods mainly focus on machine learning techniques trained using SMART measurements. However, such methods are usually expensive to use in practice and can only be applied to a limited types of errors with a limited scale. We collected more than 23-million storage events from 87 deployed NetApp-ONTAP systems managing 14,371 disks for two years and propose a lightweight training-free storage error forecasting method SEFEE. SEFEE employs Tensor Decomposition to directly analyze storage error-event logs and perform online error prediction for all error types in all storage nodes. SEFEE explores hidden spatio-temporal information that is deeply embedded in the global scale of storage systems to achieve record breaking error forecasting accuracy with minimal prediction overhead.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125564361","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 : 2020-11-01DOI: 10.1109/SC41405.2020.00059
Marco Minutoli, Prathyush Sambaturu, M. Halappanavar, Antonino Tumeo, A. Kalyanaraman, A. Vullikanti
Preventing and slowing the spread of epidemics is achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have shown to be effective but are limited by computational costs. In this work, we present PREEMPT, a new approach for intervention via maximizing the influence of vaccinated nodes on the network. We prove submodular properties associated with the objective function of our method so that it aids in construction of an efficient greedy approximation strategy. Consequently, we present a new parallel algorithm based on greedy hill climbing for PREEMPT, and present an efficient parallel implementation for distributed CPU-GPU heterogeneous platforms. Our results demonstrate that PREEMPT is able to achieve a significant reduction (up to 6.75×) in the percentage of people infected and up to 98% reduction in the peak of the infection on a city-scale network. We also show strong scaling results of PREEMPT on up to 128 nodes of the Summit supercomputer. Our parallel implementation is able to significantly reduce time to solution, from hours to minutes on large networks. This work represents a first-of-its-kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.
{"title":"PREEMPT: Scalable Epidemic Interventions Using Submodular Optimization on Multi-GPU Systems","authors":"Marco Minutoli, Prathyush Sambaturu, M. Halappanavar, Antonino Tumeo, A. Kalyanaraman, A. Vullikanti","doi":"10.1109/SC41405.2020.00059","DOIUrl":"https://doi.org/10.1109/SC41405.2020.00059","url":null,"abstract":"Preventing and slowing the spread of epidemics is achieved through techniques such as vaccination and social distancing. Given practical limitations on the number of vaccines and cost of administration, optimization becomes a necessity. Previous approaches using mathematical programming methods have shown to be effective but are limited by computational costs. In this work, we present PREEMPT, a new approach for intervention via maximizing the influence of vaccinated nodes on the network. We prove submodular properties associated with the objective function of our method so that it aids in construction of an efficient greedy approximation strategy. Consequently, we present a new parallel algorithm based on greedy hill climbing for PREEMPT, and present an efficient parallel implementation for distributed CPU-GPU heterogeneous platforms. Our results demonstrate that PREEMPT is able to achieve a significant reduction (up to 6.75×) in the percentage of people infected and up to 98% reduction in the peak of the infection on a city-scale network. We also show strong scaling results of PREEMPT on up to 128 nodes of the Summit supercomputer. Our parallel implementation is able to significantly reduce time to solution, from hours to minutes on large networks. This work represents a first-of-its-kind effort in parallelizing greedy hill climbing and applying it toward devising effective interventions for epidemics.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589924","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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