Energy Efficient Cluster Usage

Introduction

Energy consumption of data centers has been increasing continuously throughout the last decade. In 2020, the energy consumption of all data centers in Germany amounted to around 3 percent of the total electricity produced. Accompanying this large energy consumption are large-scale emissions of CO2 to the atmosphere and thus significant contributions to climate change. To illustrate this, an average compute job running on a single node for one day may easily consume 10 kWh or even more. That translates roughly to brewing 700 cups of coffee. Assuming that a typical bwHPC cluster has a few hundred compute nodes, this amounts to the energy consumption of a village for each cluster.

Although a large amount of this energy consumption is an intrinsic requirement of running large HPC clusters (even when ist processors are idle, a cluster uses a lot of energy), efficient use of the available resources is important. Using as many resources as possible does not make a power user. Using them wisely does. In the following, a basic introduction to some of the most important aspects of energy-efficient HPC usage from a user perspective is given.

We can generally distinguish three tasks when optimizing for running HPC jobs efficiently.

→ What do I want to do and why do I need an HPC Cluster for it?

→ How many and which kind of hardware resources do I require for it?

→ How do I optimize my code to use these resources most efficiently?

What do I want to do and why do I need an HPC Cluster for it?

The bwHPC clusters are used to almost full capacity, and running a job on an HPC node consumes a lot of energy, as shown above. Therefore, users are requested to run only necessary jobs.

Please consider testing new setups and their output for validity prior to submitting jobs that require lots of resources. This also includes projects where a lot of (smaller) similar jobs are submitted.

Make sure to double-check your jobs prior to the submission, having to discard the output data of an HPC project due to faulty input files is wasting a lot of computational resources.

Finally, identifying the specific resource requirements for a given job is important to allocate the optimal your compute job, and to decide if an HPC cluster is needed at all.

How many and which kind of hardware resources do I require for it

Resource allocation is a crucial part when working on an HPC cluster. As this is dependent on both the job as well as the specific cluster hardware and architecture available.

A small number of jobs and few resources

→ Submit to the scheduler. No extended testing and resource scaling analysis are needed.

Medium-sized projects

→ Run only necessary jobs: Please consider testing new setups and their output for validity prior to submitting a huge amount of similar jobs

→ Start small: Run your problem on a small set of resources first.

→ Use the proper tools for development: If you develop your own code, please use the proper tools for debugging and parallel performance analysis. See: Development.

→ A look at the job feedback can help you determine if you are using the cluster efficiently

Large projects

→ Same approach as for medium-sized projects.

→ Run a scaling analysis for your project with regard to how many resources work best. See: Scaling.

Many short jobs

→ Handling via the scheduler is inefficient.

→ Simple parallelization by hand is advisable. See: A basic introduction to Parallel Programming.

How do I optimize my code to use these resources most efficiently?

The above recommendations will help use the cluster resources more efficiently. Regarding software development, power efficiency correlates obviously heavily with computing performance, but also with memory usage, i.e. the amount of memory used, but also memory efficiency.

Here, we have gathered a few results based on other research: → Use an efficient programming language such as Rust, C, and C++ -- well any compiled language. Do not use any interpreted language like Perl or Python. Since Machine Learning is a hot topic, this deserves a few words: Any ML-Python code using Tensorflow or other libraries will make heavy usage of NumPy and other math packages, which will use C-based implementations. Please make sure, you use the provided Python modules, which are optimized to use Intel MKL and other mathematical libraries.

Further reading: Rui Pereira, et al: "Energy efficiency across programming languages: how do energy, time, and memory relate?", SLE 2017: Proc. of the 10th ACM SIGPLAN Int. Conf. on SW Language Eng., Oct. 2017, pp. 256–267, doi:10.1145/3136014.3136031

→ Analyse memory access patterns

→ For small tight loops checking for locks, use the pause instruction.

Summary: General Recommendations

• Choose the most efficient algorithms for the given problem
• Run only necessary jobs: Please consider testing new setups and their output for validity prior to submitting a huge amount of similar jobs
• Start small: Run Your problem on a small number of parallel entities (be it processes or threads) first.
• Estimate the runtime of the parallel job as exactly as possible to increase the efficiency of the scheduling of the whole system
• Use the proper tools for development: If You develop your own code, please use the proper tools for debugging and parallel performance analysis. More information is available on the bwHPC Wiki.
• A look at the job feedback can help you determine if you are using the cluster efficiently