CCF C类会议HiPC2024即将截稿(附投稿交流群)

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会议全称：IEEE International Conference on High Performance Computing, Data, and Analytics

录用率：2021年24.83%

CCF分级：计算机体系结构/并行与分布计算/存储系统C

截稿时间：2024/6/23

录用通知时间：2024/9/13

官网链接：https://hipc.org/

征稿范围：

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):

• 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;