Best HPC Software for Linux
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Compare the Top HPC Software for Linux as of October 2025
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What is HPC Software for Linux?
High-Performance Computing (HPC) software are applications designed to maximize computational power, enabling complex and resource-intensive tasks to be executed efficiently. These programs optimize parallel processing, often leveraging supercomputers or distributed computing clusters to solve problems in fields like scientific research, engineering, and data analytics. HPC software includes components for workload management, data communication, and performance tuning, ensuring scalability and efficient resource utilization. Examples include simulation software, machine learning frameworks, and tools for weather modeling or molecular dynamics. By harnessing advanced algorithms and hardware, HPC software accelerates computation, reducing the time required for tasks that would otherwise take weeks or months on conventional systems. Compare and read user reviews of the best HPC software for Linux currently available using the table below. This list is updated regularly.
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UberCloud
Simr (formerly UberCloud)
Simr (formerly UberCloud) is a cutting-edge platform for Simulation Operations Automation (SimOps). It streamlines and automates complex simulation workflows, enhancing productivity and collaboration. Leveraging cloud-based infrastructure, Simr offers scalable, cost-effective solutions for industries like automotive, aerospace, and electronics. Trusted by leading global companies, Simr empowers engineers to innovate efficiently and effectively. Simr supports a variety of CFD, FEA and other CAE software including Ansys, COMSOL, Abaqus, CST, STAR-CCM+, MATLAB, Lumerical and more. Simr automates every major cloud including Microsoft Azure, Amazon AWS, and Google GCP. -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly.
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