Improving the Performance and Energy Efficiency of HPC Applications Using Autonomic Computing Techniques

Research topic and goals

Behaviour 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 behaviour 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 2018/2019

We performed analysis of power capping mechanisms, RAPL in particular, and of their interaction with application-level variations. We identified potential retroaction loops in that context, and currently focus on a particular representative autonomic loop : deciding at runtime, dynamically, on the sufficient/minimal level of power capping  such that the application performance remains maximum. The range of flexibility/elasticity typically comes from some phases of an application involving more input/output, slowing down computations, and therefore the same performance can be achieved while slowing down the processor, through lower power cap.

Preliminary work completed at Argonne to instrument a collection of HPC applications to report progress: an application-specific measure of online performance. These progress reports will be used as a sensor in future work.

Visits and meetings

We schedule regular video meetings between the different members of the project. Swann Perarnau (ANL) visited Grenoble for a week to make progress on the project in April 2018.

Impact and publications

None yet.

    Future plans

    Short term plans are to finalize the design of controllers for this feedback loop : we consider a range of controllers, from simple intuitive algorithms, to model-based approaches involving control theoretical approaches.

    We then plan to perform a series of experimental evaluations in order to characterize and compare the different controllers w.r.t. the gain in power consumption as well as properties of the controllers (convergence, stability).