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Track Slurm Jobs with PIKA

PIKA is a hardware performance monitoring stack to identify inefficient HPC jobs. Users of ZIH systems have the possibility to visualize and analyze the efficiency of their jobs via the PIKA web interface.


To understand this guide, it is recommended that you open the web interface in a separate window. Furthermore, you should have submitted at least one real HPC job at ZIH systems.

If you are outside the TUD network, you will need to establish a VPN connection. For more information on our VPN and how to set it up, please visit the corresponding ZIH service catalog page.


PIKA consists of several components and tools. It uses the collection daemon collectd, InfluxDB to store time-series data and MariaDB to store job metadata. Furthermore, it provides a powerful web interface for the visualization and analysis of job performance data.

The analysis of HPC jobs in PIKA is designed as a top-down approach. Starting from the table view, you can either analyze running or completed jobs. You can navigate from groups of jobs with the same name to the metadata of an individual job and finally investigate the job’s runtime metrics in a timeline view.

To find jobs with specific properties, you can sort the table by any column, e.g., by consumed CPU hours to find jobs where an optimization has a large impact on the system utilization. Additionally, there is a filter mask to find jobs that match several properties. When a job has been selected, the timeline view opens.

Timeline Visualization

PIKA provides timeline charts to visualize the resource utilization of a job over time. After a job is completed, timeline charts can help you to identify periods of inefficient resource usage. However, they are also suitable for the live assessment of performance during the job’s runtime. In case of unexpected performance behavior, you can cancel the job, thus avoiding long execution with subpar performance.

The following timeline visualization shows a job with 840 cores, spread over 35 (dual-socket Haswell) nodes that have been allocated for exclusive use.

Timeline Visualization

PIKA provides the following runtime metrics:

Metric Hardware Unit Sampling Frequency
CPU Usage CPU core 30s
IPC (instructions per cycle) CPU core 60s
FLOPS (normalized to single precision) CPU core 60s
Main Memory Bandwidth CPU socket 60s
CPU Power CPU socket 60s
Main Memory Utilization node 30s
I/O Bandwidth (local, Lustre) node 30s
I/O Metadata (local, Lustre) node 30s
GPU Usage GPU device 30s
GPU Memory Utilization GPU device 30s
GPU Power Consumption GPU device 30s
GPU Temperature GPU device 30s

Each monitored metric is represented by a timeline, whereby metrics with the same unit and data source are displayed in a common chart, e.g., different Lustre metadata operations. Each metric is measured with a certain granularity concerning the hardware, e.g. per hardware thread, per CPU socket or per node. Most metrics are recorded every 30 seconds except IPC, FLOPS, Main Memory Bandwidth and Power Consumption. The latter are determined every 60 seconds, as they are a combination of different hardware counters, which leads to a higher measurement overhead. Depending on the architecture, metrics such as normalized FLOPS (2 x double-precision + 1 x single-precision) can require multiplexing, since single and double precision FLOPS cannot be measured simultaneously. The sampling frequency cannot be changed by the user.


Be aware that CPU socket or node metrics can share the resources of other jobs running on the same CPU socket or node. This can result e.g., in cache perturbation and thus a sub-optimal performance. To get valid performance data for those metrics, it is recommended to submit an exclusive job (--exclusive)!

If the current partition supports simultaneous multithreading (SMT) the maximum number of hardware threads per physical core is displayed in the SMT column. The Slurm configuration on ZIH systems disables SMT by default. Therefore, in the example below, only a maximum CPU usage of 0.5 can be achieved, since PIKA combines two hardware threads per physical core. If you want to use SMT, you must set the Slurm environment variable SLURM_HINT=multithread. In this case, srun distributes the tasks to all available hardware threads, thus a CPU usage of 1 can be reached. However, the SMT configuration only refers to the srun command. For single node jobs without srun command the tasks are automatically distributed to all available hardware threads.

SMT Mode


To reduce the amount of recorded data, PIKA summarizes per hardware thread metrics to the corresponding physical core. In terms of simultaneous multithreading (SMT), PIKA only provides performance data per physical core.

The following table explains different timeline visualization modes. By default, each timeline shows the average value over all hardware units (HUs) per measured interval.

Visualization Mode Description
Maximum maximal value across all HUs per measured interval
Mean mean value across all HUs per measured interval
Minimum minimal value across all HUs per measured interval
Mean + Standard Deviation mean value across all HUs including standard deviation per measured interval
Best best average HU over time
Lowest lowest average HU over time

The visualization modes Maximum, Mean, and Minimum reveal the range in the utilization of individual HUs per measured interval. A high deviation of the extrema from the mean value is a reason for further investigation, since not all HUs are equally utilized.

To identify imbalances between HUs over time, the visualization modes Best and Lowest are a first indicator how much the HUs differ in terms of resource usage. The timelines Best and Lowest show the recorded performance data of the best/lowest average HU over time.

Footprint Visualization

Complementary to the timeline visualization of one specific job, statistics on metadata and footprints over multiple jobs or a group of jobs with the same name can be displayed with the footprint view. The performance footprint is a set of summarized run-time metrics that is generated from the time series data for each job. To limit the jobs displayed, a time period can be specified.

To analyze the footprints of a larger number of jobs, a visualization with histograms and scatter plots can be used. PIKA uses histograms to illustrate the number of jobs that fit into a category or bin. For job states and job tags there is a fixed number of categories or values. For other footprint metrics PIKA uses a binning with a user-configurable bin size, since the value range usually contains an unlimited number of values. A scatter plot enables the combined view of two footprint metrics (except for job states and job tags), which is particularly useful for investigating their correlation.



If you wish to perform your own measurement of performance counters using performance tools other than PIKA, it is recommended to disable PIKA monitoring. This can be done using the following Slurm flags in the job script:

#SBATCH --exclusive
#SBATCH --constraint=no_monitoring

Note: Disabling PIKA monitoring is possible only for exclusive jobs!

Known Issues

The PIKA metric FLOPS is not supported by the Intel Haswell CPU architecture. However, PIKA provides this metric to show the computational intensity. Do not rely on FLOPS on Haswell! We use the event AVX_INSTS_CALC which counts the insertf128 instruction.

Case Studies

Idle CPUs

CPU Idle

Blocking I/O Operations

I/O Blocking

Memory Leaks

Memory Leaking