AWS HPC Blog
Author: Carlos Manzanedo Rueda
Carlos Manzanedo Rueda is a Principal Solutions Architect for AWS, leading the Global Flexible Compute Spot. Carlos’s goal is helping customers successfully achieve large and complex cloud transformation projects, and helping them to optimize their workloads and operational costs. He is passionate about Distributed computing, open-source, and formal optimization methods. Before joining AWS he spent 14 years working as Head of engineering for Tier 1 banks innovating Grid Computing and Risk & Analytics.
HTC-Grid – examining the operational characteristics of the high throughput compute grid blueprint
The HTC-Grid blueprint meets the challenges that financial services industry (FSI) organizations for high throughput computing on AWS. This post goes into detail on the operational characteristics (latency, throughput, and scalability) of HTC-Grid to help you to understand if this solution meets your needs.
Efficient and cost-effective rendering pipelines with Blender and AWS Batch
This blog post explains how to run parallel rendering workloads and produce an animation in a cost and time effective way using AWS Batch and AWS Step Functions. AWS Batch manages the rendering jobs on Amazon Elastic Compute Cloud (Amazon EC2), and AWS Step Functions coordinates the dependencies across the individual steps of the rendering workflow. Additionally, Amazon EC2 Spot instances can be used to reduce compute costs by up to 90% compared to On-Demand prices.
Cloud-native, high throughput grid computing using the AWS HTC-Grid solution
We worked with our financial services customers to develop an open-source, scalable, cloud-native, high throughput computing solution on AWS — AWS HTC-Grid. HTC-Grid allows you to submit large volumes of short and long running tasks and scale environments dynamically. In this first blog of a two-part series, we describe the structure of HTC-Grid and its objective to provide a configurable blueprint for HPC grid scheduling on the cloud.