Authors are invited to submit manuscripts that present novel and impactful research in all areas of parallel and distributed processing. Works focusing on emerging technologies, interdisciplinary work spanning multiple IPDPS focus areas, and novel open-source artifacts are welcome. Topics of interest include but are not limited to the following areas:

Parallel and Distributed Algorithms for Computational Science:
This track focuses on parallel and distributed (to include cloud, edge, and fog computing) algorithms arising in the context of execution of computational science methods. Examples of computations forming these workloads include structured and unstructured grids, dense and sparse linear algebra computations, spectral methods, and n-body computations. Also included in this track are algorithmic and theory contributions that are workload agnostic but specific to tightly coupled systems, such as those supporting communication, synchronization, and power management.  

Parallel and Distributed Algorithms for Data Science:
This track focuses on parallel and distributed (to include cloud, edge, and fog computing) algorithms arising in the context of execution of data science methods, including machine learning, data mining, graph computations, clustering, visualization, and other forms of data analytic methods. Also included in this track are algorithmic and theory contributions that are workload agnostic but specific to loosely coupled systems, such as those for management of distributed resources, and those related to distributed data and transactions as well as mobility. 

Applications:
The focus of this track is on papers that develop applications to solve problems using parallel and distributed computing concepts. Papers submitted to this track are expected to incorporate considerations specific to the target application area.Topics may include the design, implementation, and evaluation of parallel and distributed applications. If the primary focus is on novel or generalizable methodologies for modeling, analyzing, or evaluating performance, the paper might be better suited to the  to the "Measurements, Modeling, and Experiments" track.

Programming Models, Compilers, and Runtime Systems:
This track covers topics ranging from the design of parallel programming models and paradigms to languages and compilers supporting these models and paradigms, to runtime and middleware solutions. Software that is close to the application (as opposed to the bare hardware) but not specific to an application is included – examples include frameworks targeting cloud and distributed systems; application frameworks for fault tolerance and resilience; software supporting data management, scalable data analytics and similar workloads, and runtime systems for future novel computing platforms including quantum, neuromorphic, and bio-inspired computing.

System Software:
This track focuses on software that is close to the bare hardware. Topics include storage and I/O systems; system software for resource management, job scheduling, and energy-efficiency; system software support for accelerators and heterogeneous HPC computing systems; interactions between the OS and the hardware with other software layers; system software support for fault tolerance and resilience; containers and virtual machines; specialized operating systems and related support for high performance computing; and system software for future novel computing platforms including quantum, neuromorphic, and bio-inspired computing.

Architecture:
This track focuses on studies related to both existing and emerging architectures, including architectures for instruction-level and thread-level parallelism; manycore, multicores, accelerators, domain-specific and special-purpose architectures, reconfigurable architectures;  memory technologies and hierarchies; volatile and non-volatile emerging memory technologies, solid-state devices; exascale system designs; data center and warehouse-scale architectures; novel big data architectures; network and interconnect architectures; emerging technologies for interconnects; parallel I/O and storage systems; power-efficient and green computing systems; resilience, security, and dependable architectures; emerging architectural trends for machine learning, approximate computing, quantum computing, neuromorphic, analog, and bio-inspired computing.

Measurements, Modeling, and Experiments:
This track focuses on experiments and performance-oriented studies in the practice of parallel and distributed computing. “Performance” may be construed broadly to include metrics related to time, energy, power, accuracy, and resilience, for instance. Topics include: methods, experiments, and tools for measuring, evaluating, and/or analyzing performance for large-scale applications and systems; design and experimental evaluation of applications of parallel and distributed computing in simulation and analysis; experiments on the use of novel commercial or research architectures, accelerators, quantum and neuromorphic architectures, and other non-traditional systems;  innovations made in support of large-scale infrastructures and facilities; and methods for and experiences allocating and managing system and facility resources.