Bring higher-quality high performance computing products to market faster with the help of AI.
Massive high-performance compute grids designed for product design, modeling, simulation and verification can increase confidence in product quality. However, the compute infrastructures of many of our clients’ grids are struggling to keep up with the pressures of capturing and protecting market share.
Our solution uses complex models and machine learning (ML) to help clients optimize prediction of compute and storage resources for near-term scheduling and long-term demand planning, with a 95 percent accuracy. Users have the ability to orchestrate tradeoffs and meet required demands in business specific rules, allowing seamless integration between on-premise and cloud infrastructure.
Accenture’s Engineering compute solution for semiconductor delivers dramatic improvements in resource (compute, network, and storage) utilization, while optimizing tool license consumption costs and enabling faster completion of product design cycles. For more, read the brochure:
Accenture Engineering Compute optimizes utilization of public and private clouds.
Achieves faster time-to-market for complex products with intelligent scheduling of high-performance compute workloads.
A more efficient compute grid allows for more, and varied, simulations that help model, test and verify product designs fast.
Combines business aligned rules engine for applying predictions with two-tier global scheduling across on-premise & cloud (burst) environments
Enables continuous optimization of prediction models, applies in real-time to work requests based on operational data from the computer environment
Develops data center strategy, grid design & reference architectures for compute, storage & IO telemetry
Applies forecasting tools to reconcile short & long-term demand for infrastructure capacity
Provides analytical reporting for visibility into KPIs, diagnostics & detailed telemetry
Accelerate innovation by:
Reduction in time-to-markets
Improvement in throughput
Reduce total cost of ownership by:
Increase in infrastructure utilization (over baseline)
Decrease in memory and core spend