Improving the Performance and Energy Efficiency of HPC Applications Using Autonomic Computing Techniques
Research topic and goals
Behavior of HPC systems, such as performance, power consumption, thermal distribution, are increasingly hard to predict due to process variations and dynamic processors, which conflicts with conventional bulk synchronous parallelism software models such as MPI. Additionally, computing facilities are now interested in limiting computing resources by power/energy budget rather than CPU time. Such idea is also negatively affected by unpredictable behavior of modern HPC systems. Hardware-level power-limiting capability is becoming a standard feature in modern processors. Intel running average power limit (RAPL), for example, adjusts CPU core frequency (and CPU states if needed) periodically in order to limit the CPU power consumption not to exceed user-specified per-socket power budget. While RAPL, in fact, is an excellent mechanism for independent workloads, there are multiple considerations for HPC usage. The side effect of such power limiting is performance variation between cores/sockets/nodes; no mechanism to communicate between sockets or nodes. The effective range of user-specifiable power budget is limited; lower efficiency with lower power budget. They tend to be a hardware feature; not tuneable.
We propose a software-level solution to provide additional control mechanisms that increase hardware power-limiting feature and work for multiple nodes by applying autonomic computing techniques and targeting HPC workloads. We adopt an approach involving Autonomic Computing and Control Theory. We will identify significant sensors and actuators (e.g., switching on/off cores, changing CPU frequency), build adaptive models of the process to be controlled, and define robust adaptive control objectives w.r.t. appropriate metrics. We will design and experimentally validate controllers regulating consumption while ensuring performance. We will explore variants, beyond simple threshold-based control e.g., predictive control to avoid overshooting or adaptive control for robustness to the natural variability, considering costs of actions, avoiding oscillations and over-reaction, handle multiple criteria, coordinate multiple autonomic loops. Based on our previous experience in Cloud-oriented Autonomic Computing, we will generalize and explore novel issues in adapting our approaches to specificities of HPC and power management.
Results for 2019/2020
On the basis of the preliminary work done at ANL on instrumentation of HPC applications, INRIA has begun work on a range of controllers for the runtime adaptation of the Power Cap level in RAPL. A first approach is considered, re-using results on other work concerning a different problem (regulating the degree of parallelism according to synchronization cost), but which could be transferred here. Another approach involves measuring progress and power and making decisions based on predictions.
Argonne completed the design and implementation of an infrastructure to perform control experiments using Jupyter notebooks. This infrastructure can be deployed on a wide range of servers, and allows collaborators to independently implement their own control algorithm using a simple interface and a high level language. The notebook then connect remotely to the existing Argo NRM infrastructure, that keeps track of actuators, sensors and application management.
Results for 2020/2021
Sophie Cerf (INRIA) started working as an INRIA-funded postdoc on this project in October 2020. Using the Jupyter notebook infrastructure and Grid’5000, we are in the process on designing and validating control-theory based approaches to the problem of power/performance efficiency, with RAPL as the main actuator. We have identified our main objective for HPC systems: as applications dynamically undergo variations in workload, due to phases or data/compute movement between devices, one can dynamically adjust power across compute elements to save energy with limited and controllable impact on application performance. We are also focusing on leveraging preliminary work from Argonne on periodical monitoring of application progress.
We are in the process of publishing our first study, using a preliminary offline identification process to derive a model of the dynamics of the system and a proportional-integral (PI) controller, on top of the Argo NRM infrastructure.
Results for 2021/2022
We published our first study, using a preliminary offline identification process to derive a proportional-integral (PI) controller on top of the Argo NRM infrastructure, at Euro-Par 2021 (Cerf et al. 2021). This study includes a controller applied to several Grid5000 clusters, and controlling a singe memory-bound application, using Intel RAPL for power control.
A second publication is in the works, more on the control theory aspects of the work. Following this, we will work on expanding our control design process to allow for various types of applications and applications with phases in particular. The goal is to improve the NRM infrastructure and the controller design to detect phases in application performance and adapt the control to them.
Valentin Reis (ANL) left Argonne for Groq in March 2021. Idriss Daoudi (ANL) started working as an ANL-funded postdoc on this project in October 2021. Sophie Cerf (INRIA) became an INRIA research scientist in Lille in October 2021.
Results for 2022/2023
We published our second publication, involving results from the MSc internship of Ismail Hawila (Hawila et al. 2022). Observing limitations of our previous work regarding both modeling (nonlinear models with numerous parameters) and control performance (mainly instability caused by platform variations), we developed a novel adaptive control that is robust to the variety of execution platforms while maintaining the existing global goals of energy management. It improves the reusability and portability of our controller. We evaluated, on a real system using the Grid’5000 testbed, the robustness of the control to changes in initial parameters and to disturbances, and we compared it with the previous proportional-integral (PI) control. Our adaptive control approach allows for up to 25% energy savings.
We continue to improve the NRM infrastructure for robustness, and the evaluation of our control schemes towards supporting more applications and more hardware control knobs.
Results for 2023/2024
A MSc internship at Inria Lille, co-advised by Sophie Cerf (INRIA) and Raphaël Bleuse (INRIA) at Inria Grenoble, was performed by Kouds Halitim, on “Enhancing Efficiency through Control theory in Compute-Intensive Applications”. It extended on previous work by adding a compute-intensive benchmark (NAS EP) to possible workloads. Modeling was carried-out on extensive experimentations on various Grid’5000 clusters, following the identification techniques from Control Theory. The approach additionally explored novel control strategies, in the form of cascaded control such as PI control and MPC to enable better robustness e.g., w.r.t. noisy signals from sensors. INRIA hired an engineer, Jonathan Bleuzen, as a support for experimentations around NRM, and automation of identification and validation of controller on Grid’5000. The NRM infrastructure continues to improve, a new software release with better stability and event management, as well as additional actuators is scheduled for early 2024.
We are working on a journal paper to be submitted, on the methodological and instrumentation aspects of implementing managers, based on Control Theory or Reinforcement Learning, in HPC systems, based on our experiences, and documenting the concrete problems of implementing sensors and actuators, as well as integrating the controllers.
Visits and meetings
We schedule regular video meetings between the different members of the project.
Eric Rutten (INRIA) visited ANL for two days to make progress on the project on April 18-19 2019.
Swann Perarnau (ANL) visited INRIA in December 2023.
Impact and publications
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Hawila, Ismail, Sophie Cerf, Raphaël Bleuse, Swann Perarnau, and Éric Rutten. 2022. “Adaptive Power Control for Sober High-Performance Computing.” In 6th IEEE Conference on Control Technology and Applications. IEEE.
@inproceedings{hawila2022ccta, author = {Hawila, Ismail and Cerf, Sophie and Bleuse, Rapha{\"e}l and Perarnau, Swann and Rutten, {\'E}ric}, booktitle = {6th IEEE Conference on Control Technology and Applications}, title = {Adaptive Power Control for Sober High-Performance Computing}, year = {2022}, publisher = {IEEE} } -
Cerf, Sophie, Raphaël Bleuse, Valentin Reis, Swann Perarnau, and Éric Rutten. 2021. “Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach.” In Euro-Par 2021: Parallel Processing. Springer International Publishing. doi:10.1007/978-3-030-85665-6_21.
@inproceedings{cerf2021europar, title = {{Sustaining Performance While Reducing Energy Consumption: A Control Theory Approach}}, author = {Cerf, Sophie and Bleuse, Rapha{\"e}l and Reis, Valentin and Perarnau, Swann and Rutten, {\'E}ric}, booktitle = {Euro-Par 2021: Parallel Processing}, year = {2021}, doi = {10.1007/978-3-030-85665-6_21}, publisher = {Springer International Publishing} }
Future plans
We now have a reasonable collection of controller designs to experiment on, and are focusing on improving our designs towards a wider range of benchmarks. Our experience is that adding applications to monitor or control tends to highlight shortcomings in the controller designs or the signals used to characterize performance. Given the expected architectures on future systems, we are also planning to evaluate different actuators than RAPL (i.e. accelerator power capping). We also plan to consider more elaborate control techniques, to obtain controllers that are more robust to generic applications, including phases, tracing OpenMP or MPI, or using performance counters in monitoring.