Hardware/Software Co-design for Sparse and Irregular Applications

  in Special Issue   Posted on July 20, 2020

Information for the Special Issue

Submission Deadline: Mon 01 Mar 2021
Journal Impact Factor : 1.362
Journal Name : Parallel Computing
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/parallel-computing/call-for-papers/hardwaresoftware-co-design-for-sparse-and-irregular-appl
Journal & Submission Website: https://www.journals.elsevier.com/parallel-computing

Special Issue Call for Papers:

Special issue on Hardware/Software Co-design for Sparse and Irregular Applications

Guest Editors: Antonino Tumeo, Flavio Vella

1. Call for Papers

Many well established or emerging high-performance computing applications are said to exhibit irregular behaviors, because they present fine-grained unpredictable memory access patterns, irregularity in the contro control structures, and/or network communication of variable sizes. They operate on ever growing, not rigidly structured, data sets, thus they have significant degree of parallelism. However, for these same reasons they may also be synchronization intensive, and very difficult to load balance on parallel architectures. 

Irregular applications pertain both to well established and emerging fields, such as Computer Aided Design (CAD), bioinformatics, data mining, machine learning on sparse data structures (e.g. Graph Neural Networks), analysis of social, transportation, communication and other types of networks, and computer security. Additionally, the newest high-performance computing applications really are converging towards a mix of conventional scientific simulation, machine learning and data analytics, hence combining regular with irregular irregular phases. 

Current high-performance systems rely on data locality, regular computations, and easily partitionable data sets to exploit parallelism and increase performance. This happens both at the hardware level, where general purpose and specialized processors (e.g., GPUs, or domain-specific accelerators mostly focused to machine learning) aim at reaching high flop-rates with vector or tensor units for computation on dense data structures and at reducing latencies with large caches and deep memory hierarchies, and at the software level, where most of the runtimes, libraries, and/or algorithms mostly exploit data partitioning and data movement reduction. However, the current solutions that focus on leveraging these features do not cope well with the more complex sparse data structures and behaviors of irregular applications. 

Addressing the issues of irregular applications on current and future system architectures will become critical to solve the scientific challenges of the next few years, and will require a collaborative codesign process of both the hardware and the software.

This special issue seeks works that explore hardware/software co-design approaches for developing and optimizing execution of irregular applications on high-performance systems, at all levels of the stack: micro- and systemarchitecture, network, languages, libraries, runtimes, compilers, analysis, algorithms. Specifically, this issue aims at collecting those novel research solutions that connect domain-specific architectures and emerging high-performance applications that exhibit irregular behaviors. 

Topics of interest, of both theoretical and practical significance, include but are not limited to: 

  • Micro- and System-architectures 
  • Network and memory architectures 
  • Manycore, hybrid, and custom architectures (Tensor architectures, GPUs, FPGAs, near-memory designs) 
  • Heterogeneous approaches and methods for exploiting domain-specific systems 
  • Modeling, evaluation and characterization of domain-specific architectures for memory intensive and irregular applications also from theoretical perspective 
  • Innovative algorithmic techniques 
  • Parallelization techniques for sparse data structures 
  • Languages and programming models 
  • Library and runtime support 
  • Compiler and analysis techniques 
  • Case studies of irregular applications (e.g. Knowledge Graphs, Machine Learning on sparse data-structures, Data Mining, Security, Bioinformatics) 
  • Support for irregular applications on novel computing architectures (quantum, neuromorphic)

2. Important dates 

  • 1 Dec. 2020 – Submissions open 
  • 1 Mar. 2021 – Submissions 
  • 1 Sep. 2021 – Acceptance notification