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At ²ÝÁñÉçÇø, we are constantly looking for ways to optimize the user experience of running workloads in the cloud by providing performance and cost optimization out of the box. Heterogeneous clusters and Dynamic scaling are the constructs designed to solve two fundamental problems: 

• Which hardware is best suited for my workload
• How do I ensure cost efficiency running the workload in a performant fashion 

One of the key benefits which ²ÝÁñÉçÇø Cloud offers is providing our customers with a selection of optimized computing specific to each workload within the complex chip design lifecycle. We tested and benchmarked hundreds of compute virtual machine types available in the public cloud to enable a small subset optimized for specific EDA workloads.  This selection of optimized compute enables customers to quickly establish the optimal compute, thereby reducing the wastage of resources and therefore cost optimization.  

In a larger cluster, such groups of optimized hardware can be grouped together as sub-clusters of a heterogeneous cluster in an automated fashion. This allows for a standardized cluster that consists of optimized hardware for distinct EDA workloads with the operating system and EDA software along with it. Diverse EDA jobs can be submitted to the same cluster but directed to the most appropriate sub-clusters with further specifications of resource strings.

Heterogenous Clusters

Heterogenous clusters in ²ÝÁñÉçÇø Cloud provide the flexibility for users to select the virtual machine type which best suits the application needs in a standardized cluster, enabling said clusters to handle a wide range of EDA workloads simultaneously. More importantly, the management of heterogeneous clusters is completely user driven through an intuitive web interface.  

Heterogeneous Clusters with EDA-optimized hardware are further augmented with Dynamic Scaling of compute resources in ²ÝÁñÉçÇø Cloud.

Dynamic Scaling

Dynamic cluster scaling is the technique used to adjust the number of nodes active in a cluster based on its current workloads. This is extremely important in the cloud context where the cluster can adapt to changing workloads, ensuring that the jobs are completed as efficiently as possible, and provide cost optimization while doing so.

These two features are available in ²ÝÁñÉçÇø Cloud in an intuitive fashion through the Web Portal, allowing designated users to select a private vs shared cluster, defining the heterogenous cluster and its limit, and enabling dynamic clustering to ensure performance at an optimized cost.