Data Science (DS)

Big data, data stream, data engineering, data analytics, time series, neural networks, big data frameworks (Hadoop, Flink, and Spark, Storm, and Kafka), and machine learning framework (Torch, Keras, and TensorFlow).

Internet of Things (IoT)

Agriculture, biology, health care, industry, stream and complex event processing, mobile and embedded systems.

Parallel Programming (PP)

Parallel Patterns, Algorithmic Skeletons, Task Scheduling, TBB, FastFlow, OpenMP, MPI, HPX, CUDA, OpenCL, and OpenACC.

Programming Languages (PL)

C/C++, Python, Shell Script, Rust, Java

Cloud Computing (CC)

IaaS cloud platforms (OpenStack, OpenNebula, and CloudStack), performance evaluation, deployment optimizations (virtualization, containers, networking, storage), and PaaS platforms.

Parallel and Distributed Systems (PDS)

Benchmarking, distributed platforms, high availability, fault tolerance, resource management, and energy efficiency.

High-Performance Architectures (HPA)

Cluster computing, cloud infrastructure, multi-core systems, and hardware accelerators (GPU, FPGA).

Computer Networking (CN)

Application protocol, high-speed network, virtual network, and cloud network tools

Parallel Programming Effort (PPE)

Parallelism abstractions, high-level parallel programming, programming effort, coding productivity‚Äč, metrics, and methodologies.

Compilers and Optimizations (CO)

Parallel code generation, code autotuning, source-to-source transformation, static code analysis 

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