Efficiently tackle high-volume workloads. From rendering to training ML models, DCP brings serious computational power without the high cost of other solutions. Start with DCP today
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. With DCP, businesses can greatly reduce completion times 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 process 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, great 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 process 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, great 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 a variety of models, massively reducing the cost of GPUs while accelerating distributed architectures. Scale 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 makes 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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DCP’s team of experts can help qualify the suitability and returns of any program augmented with distributed computing.
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