Unlocking genomics workloads on the Cloud

Опубликовано 15 сентября 2020, 16:11

Genomics data is growing at an incredible rate and currently there is a rise in several sequencing projects across Europe. Collectively, many organizations in the US are also investing in the collection/sequencing of data for large populations.

As a result, genomic workloads are gaining significant traction with large bursty computations requirements. GCP is uniquely positioned to unlock some of the most computationally demanding workloads by offering large memory instances that can handle metagenomics workloads or the ability to burst to hundreds of thousands of cores to run a large GWAS study.

Learn about how such workloads can be deployed and scaled on GCP, then deep-dive into some of the challenges facing genomics workloads and some of the approaches used to solve them. Review the use-cases and designs that we have with existing customers on GCP. Lastly, learn about how our customers are running large GAWS studies and lowered the cost by taking advantage of PVMs.

Speakers: Hussain Al Muscati, Hatem Nawar

Watch more:
Google Cloud Next ’20: OnAir → goo.gle/next2020

Subscribe to the GCP Channel → goo.gle/GCP

#GoogleCloudNext

CMP219
product: Compute Engine, Migrate for Compute Engine; fullname: Hussain Al Muscati, Hatem Nawar;


event: Google Cloud Next 2020; re_ty: Publish; product: Cloud - Compute - Migrate for Compute Engine, Cloud - Compute - Compute Engine; fullname: Hussain Al Muscati, Hatem Nawar;