Adaptive Computing
Adaptive Computing is an enterprise software company that provides workload orchestration and resource management platforms for High performance computing (HPC), cloud, and Artificial Intelligence (AI) infrastructure.
- Workload orchestration and scheduling for HPC clusters and supercomputing environments (HPC infrastructure management).
- Hybrid and multi-cloud workload management for on-premises (on-prem) and cloud resources (cloud orchestration).
- Policy-based job scheduling, resource allocation, and Quality of Service (QoS) controls for compute-intensive workloads (IT operations management).
- Tools for managing AI, Machine Learning (ML), and data-intensive workloads across diverse infrastructure (AI/ML infrastructure management).
- Professional services and support for planning, deploying, and operating large-scale compute environments (consulting and managed services).
More About Adaptive Computing
Adaptive Computing focuses on workload orchestration and resource management software used by enterprises, research institutions, and government organizations that operate HPC, AI, and data-intensive environments. Its platforms are designed to schedule and manage large volumes of batch and interactive jobs across clusters, supercomputers, and hybrid cloud infrastructure, aligning compute consumption with business or institutional policies.
The company’s core offerings are positioned around HPC workload management (HPC infrastructure management), where its software coordinates how jobs are queued, prioritized, and executed on shared compute resources. This includes capabilities such as multi-dimensional resource scheduling, project and user-level quotas, fair-share policies, and workload accounting. These features are used in environments where thousands or millions of cores are shared across teams, departments, or projects, and where administrators require deterministic control over how resources are consumed.
Adaptive Computing also operates in the hybrid and multi-cloud orchestration category (cloud orchestration), providing tools that extend scheduling and policy control from on-prem clusters into public cloud environments. This allows organizations to burst workloads to cloud resources when local clusters reach capacity or when specific instance types are only available in cloud platforms. The same policy and governance framework can be applied across on-prem and cloud, enabling unified job submission, cost-awareness, and quota enforcement.
For AI and ML workloads (AI/ML infrastructure management), Adaptive Computing’s technology focuses on managing Graphics Processing Unit (GPU) and accelerator resources, containerized workloads, and data-intensive pipelines. The platforms commonly integrate with standard Linux-based HPC stacks, container technologies such as Docker and Kubernetes, and job submission via command-line tools, portals, or APIs. Support for common file systems and interconnects in HPC environments allows enterprises to use existing infrastructure investments while applying centralized scheduling and policy controls.
From an architectural perspective, Adaptive Computing’s solutions are typically deployed as central schedulers and management services that interface with compute nodes and job submission endpoints. The software works with standard operating systems, resource managers, and monitoring tools to capture cluster state, enforce job placement rules, and report utilization. APIs and plug-ins enable integration with enterprise IT systems, identity and access management, and reporting platforms.
In marketplace and directory terms, Adaptive Computing aligns to categories including HPC workload schedulers, hybrid cloud workload management, AI/ML infrastructure orchestration, and professional services for large-scale compute environments. Organizations use these capabilities to run simulation, modeling, analytics, AI training, and other compute-intensive applications while controlling resource usage, enforcing governance, and coordinating workloads across on-prem and cloud infrastructure.