Efficiently tackle high-volume workloads with hundreds or thousands of cores. From rendering to training ML models, DCP brings serious computational firepower without the high cost of other solutions.
High-volume tasks can consume significant computational resources for a long period of time. DCP makes it simple to schedule and orchestrate even the largest batch processes, and keep their compute costs as low as possible. Unlike other tools like Slurm, there is very minimal setup involved. The result is that you can greatly reduce your job completion times with DCP, all without managing any of the underlying systems.
Data Science
Simulations
Predictive Analytics
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Processing data from a continuous stream can result in high compute and bandwidth fees, which often prohibit the use of a centralized cloud platform. DCP makes it more cost effective to monitor these streams by distributing the load across many compute nodes - at the edge, on-prem, or in the cloud as desired. It creates a single environment across multiple computational units and hardware platforms, perfect for everything from simple analytics to complex event processing.
Predictive QA
IoT Analytics
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Anomaly Detection
Processing data from a continuous stream can result in high compute and bandwidth fees, which often prohibit the use of a centralized cloud platform. DCP makes it more cost effective to monitor these streams by distributing the load across many compute nodes - at the edge, on-prem, or in the cloud as desired. It creates a single environment across multiple computational units and hardware platforms, perfect for everything from simple analytics to complex event processing.
Predictive QA
IoT Analytics
Anomaly Detection
Smart Routing
Digital Twins
Modern algorithms are computationally expensive to train and deploy. DCP is a unique compute backend for all kinds of models, massively reducing the cost of GPUs while accelerating distributed architectures like mixture of experts training and federated learning. Scale your AI/ML initiatives faster and more economically than ever with DCP.
From standard regression models to convolutional neural networks, DCP provisions ultra-affordable GPU resources in public, private, and edge settings.
DCP make the classification of unlabeled data faster and much less costly. It is perfect for tasks like hyperparameter searches and mixture-of-experts training.
Intelligent systems that make use of agent-based models greatly benefit from DCP, able to explore a greater state space in less time and at a lower cost.
DCP's massively parallel techniques support both GPU and CPU-based inferencing. It can scale this out to both on-prem and cloud-based resources interchangeably, and makes management incredibly simple.
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Whatever program you have in mind, our team of experts can help qualify its suitability and the returns from distributed computing.
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