Compare the Top HPC Software for Linux as of October 2025

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.

  • 1
    NVIDIA GPU-Optimized AMI
    The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'
    Starting Price: $3.06 per hour
  • 2
    Amazon EC2 P4 Instances
    Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.
    Starting Price: $11.57 per hour
  • 3
    NVIDIA Modulus
    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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