Hidden Costs of GPU-Accelerated Genomics Workflows
Genomics infrastructure teams are paying a hidden cost for GPU-accelerated workflows in the cloud, with a sharp mismatch between CPU-era assumptions and GPU cost models. This is costing teams in serious infrastructure costs, with a typical short-read sequencing pipeline failing 1 in 5 attempts.
Key points
- Genomics infrastructure teams are using GPU-accelerated workflows in the cloud, but are not actively measuring the costs associated with these workflows.
- The cost model for GPU cloud infrastructure differs sharply from CPU clusters, leading to a mismatch between CPU-era assumptions and GPU cost models.
- A typical short-read sequencing pipeline fails 1 in 5 attempts, with the cost of these failures often hidden in the 'cost per sample' metric.
- Teams are managing serious GPU infrastructure for the first time, with a lack of understanding of the associated costs and reliability issues.
- Analysts say that teams need to rethink their cost accounting and reliability assumptions to accurately measure the costs of GPU-accelerated workflows.
Genomics infrastructure teams are facing a hidden cost problem with GPU-accelerated workflows in the cloud. While these workflows have improved efficiency and accuracy, they come with a sharp mismatch between CPU-era assumptions and GPU cost models. This mismatch is costing teams in serious infrastructure costs, with a typical short-read sequencing pipeline failing 1 in 5 attempts.
The 'cost per sample' metric, which is often reported upward and sits in the budget, hides the true costs of these failures. Teams are managing serious GPU infrastructure for the first time, with a lack of understanding of the associated costs and reliability issues.
Analysts say that teams need to rethink their cost accounting and reliability assumptions to accurately measure the costs of GPU-accelerated workflows. This requires a more nuanced understanding of the costs associated with these workflows, including the costs of failures and the costs of managing GPU infrastructure.
By understanding these costs, teams can make more informed decisions about their infrastructure and workflows, and avoid the hidden costs of GPU-accelerated genomics workflows.
Sources
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