Call for Contributions

Scope and Topics

The ACM/SPEC International Conference on Performance Engineering (ICPE) is the leading international forum for presenting and discussing novel ideas, innovations, trends and experiences in the field of performance engineering.

Performance, energy efficiency and reliability are becoming central to the acceptance and sustainability of modern computing systems. Digitalization brings computing technology closer to people and manages most aspects of their life. In turn, human interaction increasingly shapes the behavior of computing systems. As a result, our systems become more complex and therefore more difficult to engineer and understand. We need to be able to manage this complexity so that our systems remain reliable, trustable and performant.

ICPE brings together researchers and practitioners to report on open problems, state-of-the-art solutions and in-progress research in performance engineering of software and systems - targeting performance and associated quality attributes such as efficiency and reliability in all phases of computing system lifecycle, from specification and development to run time and maintenance.

ICPE deals with performance and associated quality attributes across any and all application domains. Here is a list of examples:

  • cyber-physical domain, IoT, industrial internet
  • communication networks, wireless and mobile
  • peer-to-peer environments, ad-hoc networks
  • SOA, microservices, web-based environments
  • big data systems, stream and graph processing
  • data-intensive systems, data analytics and data science
  • machine learning and artificial intelligence
  • HPC systems, cloud, edge, grid and fog
  • social networks, multimedia systems and applications
  • autonomous, resilient and adaptive systems and applications

We encourage contributions that help to extend the state of the art in five distinct tracks - research, industry, emerging research, artifact, demo & poster - on topics relevant to performance and associated quality attributes. In particular:

Measurement and Empirical Evaluation

  • data collection techniques, simulation, measurement, instrumentation, profiling
  • controlled experiment design, data driven experiments, diagnostics
  • data management and interchange, tool interoperability
  • statistical analysis, exploration, visualization, visual data mining
  • measuring and evaluating
    • performance and related attributes such as energy efficiency, stability or resource usage
    • dependability and related attributes such as security, resiliency or confidentiality
    • algorithmic attributes such as learning efficiency or solution quality


  • approaches and metrics for describing performance and related quality attributes
  • modeling languages and formalisms, modeling methods and tools
  • explanatory and predictive models, online models
  • model learning and extraction techniques
  • model validation and calibration
  • workload characterization

Design and Development Processes

  • requirements engineering, agile and experiment driven techniques, devops
  • performance-oriented design, software architectures, performance patterns and antipatterns
  • performance testing, root cause and bottleneck identification
  • humans in the loop, ethical concerns

Managing Systems at Runtime

  • adaptive systems, monitoring, autotuning, elasticity and autoscaling, power management
  • virtualization and consolidation, resource scheduling, capacity management
  • anomaly detection, service level (definition and monitoring)

Platform-Related Optimizations

  • parallel programming, multi-core and many-core systems
  • compiler optimizations, managed languages and runtime optimization
  • performance and efficiency of hardware accelerators and novel memory systems


  • design and standardization process
  • benchmark suites, investigative benchmarks
  • benchmark synthesis, benchmark workload generation
  • benchmarking energy efficiency, resilience, stability, security and related quality metrics