HiPC 2024 is the 31st edition of the IEEE International Conference on High Performance Computing, Data, and Analytics. HiPC serves as a forum to present current work by researchers from around the world as well as highlight activities in Asia in the areas of high performance computing and data science. The meeting focuses on all aspects of high performance computing systems, and data science and analytics, and their scientific, engineering, and commercial applications. HiPC 2024 will also explore programs that expand and enrich the conference offerings, including workshops, tutorials, Birds-of-a-Feather meetings, Student Research Symposium, and industrial sessions, which provide increased professional opportunities to conference attendees.

Authors are invited to submit original unpublished research manuscripts that demonstrate current research in all areas of high performance computing, and data science and analytics, covering all  traditional areas and emerging topics including from machine learning, big data analytics. Each submission should be submitted to one of the six tracks listed under the two broad themes of  High Performance Computing and Data Science. Up to two best paper awards will be given to outstanding contributed papers. Authors of selected high-quality papers in HiPC 2024 will be  invited to submit extended versions of their papers for possible publication in a special issue of the Journal of Parallel and Distributed Computing (JPDC).

High Performance Computing

Topics for papers include, but are not limited to the topics given under the categories below:

Algorithms

This track invites papers that describe original research on developing new parallel and distributed computing algorithms, and related advances. Examples of topics that are of interest include (but not limited to):

    New parallel and distributed algorithms and design techniques;
    Advances in enhancing algorithmic properties or providing guarantees (e.g., concurrency, data locality, communication-avoiding, asynchronous, hybrid CPU-GPU algorithms, fault tolerance, resilience,);
    Algorithmic techniques for resource allocation and optimization (e.g., scheduling, load balancing, resource management);
    Provably efficient parallel and distributed algorithms for advanced scientific computing and irregular applications (e.g., numerical linear algebra, graph algorithms, computational biology);
    Classical and emerging computation models (e.g., parallel/distributed models, quantum computing, neuromorphic and other bioinspired models).

Architecture

This track invites papers that describe original research on the design and evaluation of high performance computing architectures, and related advances. Examples of topics of interest include (but not limited to): 

    High performance processing architectures (e.g., reconfigurable, system-on-chip, many cores, vector processors);
    Networks for high performance computing platforms (e.g., interconnect topologies, network-on-chip);
    Memory, cache and storage architectures (e.g., 3D, photonic, Processing-In-Memory, NVRAM, burst buffers, parallel I/O);
    Approaches to improve architectural properties (e.g., energy/power efficiency, reconfigurable, resilience/fault tolerance, security/privacy);
    Emerging computational architectures (e.g., quantum computing, neuromorphic and other bioinspired architectures). 

Applications

This track invites papers that describe original research on the design and implementation of scalable and high performance applications for execution on parallel, distributed and accelerated platforms, and related advances. Examples of topics of interest include (but not limited to):

    Shared and distributed memory parallel applications (e.g., scientific computing, simulation and visualization applications, graph and irregular applications, data-intensive applications, science/engineering/industry applications, emerging applications in IoT and life sciences, etc.);
    Methods, algorithms, and optimizations for scaling applications on peta- and exa-scale platforms (e.g., co-design of hardware and software, heterogeneous and hybrid programming);
    Hardware acceleration of parallel applications (e.g., GPUs, FPGA, vector processors, manycore);
    Application benchmarks and workloads for parallel and distributed platforms.

Systems Software

This track invites papers that describe original research on the design, implementation, and evaluation of systems software for high performance computing platforms, and related advances. Examples of topics of interest include (but not limited to):

    Scalable systems and software architectures for high-performance computing (e.g., middleware, operating systems, I/O services);
    Techniques to enhance parallel performance (e.g., compiler/runtime optimization, learning from application traces, profiling);
    Techniques to enhance parallel application development and productivity (e.g., Domain-Specific Languages, programming environments, performance/correctness checking and debugging);
    Techniques to deal with uncertainties, hardware/software resilience, and fault tolerance;
    Software for cloud, data center, and exascale platforms (e.g., middleware tools, schedulers, resource allocation, data migration, load balancing);
    Software and programming paradigms for heterogeneous platforms (e.g., libraries for CPU/GPU, multi-GPU clusters, and other accelerator platforms).

Scalable Data Science

Topics for papers include, but are not limited to the topics given under the categories below:
Scalable Algorithms and Analytics

This track invites papers that describe original research on developing scalable algorithms for data analysis at scale, and related advances. Examples of topics of interest include (but not limited to):

    New scalable algorithms for fundamental data analysis tasks (supervised, unsupervised learning, data (pre-)processing and pattern discovery);
    Scalable algorithms that are designed to address the characteristics of different data sources and settings (e.g., graphs, social networks, sequences, data streams);
    Scalable algorithms and techniques to reduce the complexity of large-scale data (e.g., streaming, sublinear data structures, summarization, compressive analytics);
    Scalable algorithms that are designed to address requirements in different data-driven application domains (e.g., life sciences, business, agriculture);
    Scalable algorithms that ensure the transparency and fairness of the analysis;
    Case studies, experimental studies, and benchmarks for scalable algorithms and analytics;

Scaling and accelerating machine learning, deep learning, natural language processing and computer vision applications.

Scalable Systems and Software

This track invites papers that describe original research on developing scalable systems and software for handling data at scale and related advances. Examples of topics of interest include (but not limited to):

    New parallel and distributed algorithms and design techniques;
    Design of scalable system software to support various applications (e.g., recommendation systems, web search, crowdsourcing applications, streaming applications)
    Scalable system software for various architectures (e.g., OpenPower, GPUs, FPGAs).
    Architectures and systems software to support various operations in large data frameworks (e.g., storage, retrieval, automated workflows, data organization, visualization, visual analytics, human-in-the-loop);
    Systems software for distributed data frameworks (e.g., distributed file system, data deduplication, virtualization, cloud services, resource optimization, scheduling);
    Standards and protocols for enhancing various aspects of data analytics (e.g., open data standards, privacy-preserving, and secure schemes).