The ECP Proxy Application Project has an annual milestone to assess the state of ECP proxy applications and their role in the overall ECP ecosystem. Our FY22 March/April milestone (ADCD- 504-28) proposed to: Assess the fidelity of proxy applications compared to their respective parents in terms of kernel and I/O behavior, and predictability. Similarity techniques will be applied for quantitative comparison of proxy/parent kernel behavior. MACSio evaluation will continue and support for OpenPMD backends will be explored. The execution time predictability of proxy apps with respect to their parents will be explored through a carefully designed scaling study and code comparisons. Note that in this FY, we also have quantitative assessment milestones that are due in September and are, therefore, not included in the description above or in this report. Another report on these deliverables will be generated and submitted upon completion of these milestones. To satisfy this milestone, the following specific tasks were completed: Study the ability of MACSio to represent I/O workloads of adaptive mesh codes. Re-define the performance counter groups for contemporary Intel and IBM platforms to better match specific hardware components and to better align across platforms (make cross-platform comparison more accurate). Perform cosine similarity study based on the new performance counter groups on the Intel and IBM P9 platforms. Perform detailed analysis of performance counter data to accurately average and align the data to maintain phases across all executions and develop methods to reduce the set of collected performance counters used in cosine similarity analysis. Apply a quantitative similarity comparison between proxy and parent CPU kernels. Perform scaling studies to understand the accuracy of predictability of the parent performance using its respective proxy application. This report presents highlights of these efforts.
Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.
GPUs are now a fundamental accelerator for many high-performance computing applications. They are viewed by many as a technology facilitator for the surge in fields like machine learning and Convolutional Neural Networks. To deliver the best performance on a GPU, we need to create monitoring tools to ensure that we optimize the code to get the most performance and efficiency out of a GPU. Since NVIDIA GPUs are currently the most commonly implemented in HPC applications and systems, NVIDIA tools are the solution for performance monitoring. The Light-Weight Distributed Metric System (LDMS) at Sandia is an infrastructure widely adopted for large-scale systems and application monitoring. Sandia has developed CPU application monitoring capability within LDMS. Therefore, we chose to develop a GPU monitoring capability within the same framework. In this report, we discuss the current limitations in the NVIDIA monitoring tools, how we overcame such limitations, and present an overview of the tool we built to monitor GPU performance in LDMS and its capabilities. Also, we discuss our current validation results. Most of the performance counter results are the same in both vendor tools and our tool when using LDMS to collect these results. Furthermore, our tool provides these statistics during the entire runtime of the tool as a time series and not just aggregate statistics at the end of the application run. This allows the user to see the progress of the behavior of the applications during their lifetime.
Azziz, Omar A.; Cook, Jeanine C.; Vaughan, Courtenay T.; McCorquodale, Peter M.; Finkel, Hal F.; Homerding, Brian H.; Moore, Shirley M.; Mintz, Tiffany M.; Watson, Greg W.; Ramakrishnaiah, Ramakrishnaiah; Vinay, Vinay; Pavel, Robert P.; Uram, Thomas U.; Liber, Nevin L.; Lujan, Xavier E.
Proceedings of PMBS 2019: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis
In this work we investigate the dynamic communication behavior of parent and proxy applications, and investigate whether or not the dynamic communication behavior of the proxy matches that of its respective parent application. The idea of proxy applications is that they should match their parent well, and should exercise the hardware and perform similarly, so that from them lessons can be learned about how the HPC system and the application can best be utilized. We show here that some proxy/parent pairs do not need the extra detail of dynamic behavior analysis, while others can benefit from it, and through this we also identified a parent/proxy mismatch and improved the proxy application.
Proceedings of PMBS 2018: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis
Proxy applications, or proxies, are simple applications meant to exercise systems in a way that mimics real applications (their parents). However, characterizing the relationship between the behavior of parent and proxy applications is not an easy task. In prior work [1], we presented a data-driven methodology to characterize the relationship between parent and proxy applications based on collecting runtime data from both and then using data analytics to find their correspondence or divergence. We showed that it worked well for hardware counter data, but our initial attempt using MPI function data was less satisfactory. In this paper, we present an exploratory effort at making an improved quantification of the correspondence of communication behavior for proxies and their respective parent applications. We present experimental evidence of positive results using four proxy applications from the current ECP Proxy Application Suite and their corresponding parent applications (in the ECP application portfolio). Results show that each proxy analyzed is representative of its parent with respect to communication data. In conjunction with our method presented in [1] (correspondence between computation and memory behavior), we get a strong understanding of how well a proxy predicts the comprehensive performance of its parent.
Richards, David F.; Cook, Jeanine C.; Finkel, Hal F.; Junghans, Christoph J.; McCorquodale, Peter M.; Moore, Shirley M.; Aaziz, Omar R.; Juedman, Tanner J.; Vaughan, Courtenay T.; Homerding, Brian H.; Urma, Thomas U.; Bhatele, Abhinav B.; Andrade, Zavier A.; Pavel, Robert P.; Ramakrishnaiah, Vinay R.; Mintz, Tiffany M.; Watson, Greg W.
Proxy applications are a simplified means for stake-holders to evaluate how both hardware and software stacks might perform on the class of real applications that they are meant to model. However, characterizing the relationship between them and their behavior is not an easy task. We present a data-driven methodology for characterizing the relationship between real and proxy applications based on collecting runtime data from both and then using data analytics to find their correspondence and divergence. We use new capabilities for application-level monitoring within LDMS (Lightweight Distributed Monitoring System) to capture hardware performance counter and MPI-related data. To demonstrate the utility of this methodology, we present experimental evidence from two system platforms, using four proxy applications from the current ECP Proxy Application Suite and their corresponding parent applications (in the ECP application portfolio). Results show that each proxy analyzed is representative of its parent with respect to computation and memory behavior. We also analyze communication patterns separately using mpiP data and show that communication for these four proxy/parent pairs is also similar.
Dennard scaling ended a decade ago. Energy reduction by lowering supply voltage has been limited because of guard bands and a subthreshold slope of over 60mV/decade in MOSFETs. On the other hand, newly-proposed logic devices maintain a high on/off ratio for drain currents even at significantly lower operating voltages. However, such ultra low power technology would eventually suffer from intermittent errors in logic as a result of operating close to the thermal noise floor. Computational error correction mitigates this issue by efficiently correcting stochastic bit errors that may occur in computational logic operating at low signal energies, thereby allowing for energy reduction by lowering supply voltage to tens of millivolts. Cores based on a Redundant Residual Number System (RRNS), which represents a number using a tuple of smaller numbers, are a promising candidate for implementing energyefficient computational error correction. However, prior RRNS core microarchitectures abstract away the memory hierarchy and do not consider the power-performance impact of RNS-based memory addressing. When compared with a non-error-correcting core addressing memory in binary, naive RNS-based memory addressing schemes cause a slowdown of over 3x/2x for inorder/out-of-order cores respectively. In this paper, we analyze RNS-based memory access pattern behavior and provide solutions in the form of novel schemes and the resulting design space exploration, thereby, extending and enabling a tangible, ultra low power RRNS based architecture.
Continued progress in computing has augmented the quest for higher performance with a new quest for higher energy efficiency. This has led to the re-emergence of Processing-In-Memory (PIM) ar- chitectures that offer higher density and performance with some boost in energy efficiency. Past PIM work either integrated a standard CPU with a conventional DRAM to improve the CPU- memory link, or used a bit-level processor with Single Instruction Multiple Data (SIMD) control, but neither matched the energy consumption of the memory to the computation. We originally proposed to develop a new architecture derived from PIM that more effectively addressed energy efficiency for high performance scientific, data analytics, and neuromorphic applications. We also originally planned to implement a von Neumann architecture with arithmetic/logic units (ALUs) that matched the power consumption of an advanced storage array to maximize energy efficiency. Implementing this architecture in storage was our original idea, since by augmenting storage (in- stead of memory), the system could address both in-memory computation and applications that accessed larger data sets directly from storage, hence Processing-in-Memory-and-Storage (PIMS). However, as our research matured, we discovered several things that changed our original direc- tion, the most important being that a PIM that implements a standard von Neumann-type archi- tecture results in significant energy efficiency improvement, but only about a O(10) performance improvement. In addition to this, the emergence of new memory technologies moved us to propos- ing a non-von Neumann architecture, called Superstrider, implemented not in storage, but in a new DRAM technology called High Bandwidth Memory (HBM). HBM is a stacked DRAM tech- nology that includes a logic layer where an architecture such as Superstrider could potentially be implemented.
We address practical limits of energy efficiency scaling for logic and memory. Scaling of logic will end with unreliable operation, making computers probabilistic as a side effect. The errors can be corrected or tolerated, but overhead will increase with further scaling. We address the tradeoff between scaling and error correction that yields minimum energy per operation, finding new error correction methods with energy consumption limits about 2× below current approaches. The maximum energy efficiency for memory depends on several other factors. Adiabatic and reversible methods applied to logic have promise, but overheads have precluded practical use. However, the regular array structure of memory arrays tends to reduce overhead and makes adiabatic memory a viable option. This paper reports an adiabatic memory that has been tested at about 85× improvement over standard designs for energy efficiency. Combining these approaches could set energy efficiency expectations for processor-in-memory computing systems.
Machine Models and Proxy Architectures for Exascale Computing Version 2.0 Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Approved for public release; further dissemination unlimited. Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or rep- resent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors. Printed in the United States of America. This report has been reproduced directly from the best available copy. Available to DOE and DOE contractors from U.S. Department of Energy Office of Scientific and Technical Information P.O. Box 62 Oak Ridge, TN 37831 Telephone: (865) 576-8401 Facsimile: (865) 576-5728 E-Mail: reports@adonis.osti.gov Online ordering: http://www.osti.gov/bridge Available to the public from U.S. Department of Commerce National Technical Information Service 5285 Port Royal Rd Springfield, VA 22161 Telephone: (800) 553-6847 Facsimile: (703) 605-6900 E-Mail: orders@ntis.fedworld.gov Online ordering: http://www.ntis.gov/help/ordermethods.asp?loc=7-4-0#online D E P A R T M E N T O F E N E R G Y * * U N I T E D S T A T E S O F A M E R I C A SAND2016-6049 Unlimited Release Printed Abstract Machine Models and Proxy Architectures for Exascale Computing Version 2.0 J.A. Ang 1 , R.F. Barrett 1 , R.E. Benner 1 , D. Burke 2 , C. Chan 2 , J. Cook 1 , C.S. Daley 2 , D. Donofrio 2 , S.D. Hammond 1 , K.S. Hemmert 1 , R.J. Hoekstra 1 , K. Ibrahim 2 , S.M. Kelly 1 , H. Le, V.J. Leung 1 , G. Michelogiannakis 2 , D.R. Resnick 1 , A.F. Rodrigues 1 , J. Shalf 2 , D. Stark, D. Unat, N.J. Wright 2 , G.R. Voskuilen 1 1 1 Sandia National Laboratories, P.O. Box 5800, Albuquerque, New Mexico 87185-MS 1319 2 Lawrence Berkeley National Laboratory, Berkeley, California Abstract To achieve exascale computing, fundamental hardware architectures must change. The most sig- nificant consequence of this assertion is the impact on the scientific and engineering applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and deter- mine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware archi- tects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of mod- els that can help software developers prepare for exascale. In addition, through the use of proxy architectures, we can enable a more concrete exploration of how well new and evolving applica- tion codes map onto future architectures. This second version of the document addresses system scale considerations and provides a system-level abstract machine model with proxy architecture information.