Nimbus is a general purpose cloud computing system specially designed for computations with short tasks. Nimbus is developed in C++ and the API offers a data model similar to DraydLINQ and Spark. The key difference between Nimbus and its counterparts is the novel control plane abstraction called Execution Templates. For jobs with short tasks the runtime overhead becomes comparable to the task itself. For example for an application with 4ms tasks, Sparks runtime overhead is about 98%. Execution templates enable Nimbus to schedule and drive jobs with tasks as short as 100us over large number of workers at scale with negligible runtime overhead.
Traditional machine learning benchmarks such as logistic regression and k-means run 40x faster under Nimbus compared to beast available frameworks. In addition, handling short tasks opens a new class of applications, traditionally aimed for HPC clusters, into cloud computing world. Nimbus provides a simple API for physical simulations based on geometry. For example we have ported PhysBAM, a physics based simulation library, into Nimbus. Nimbus runs particle-levelset simulations within 15% of the MPI hand-tuned implementations.
Nimbus project repository including the core engine source code along with a handful of applications is available at Github. For complete installation guide please visit Building Nimbus. For a basic installation to run the interactive examples you can issue make at the Nimbus root:
$ cd <path-to-nimbus-root>
$ make
After you download and build Nimbus you should be able to run simple examples on your local machine. There are few examples that you can run
Quick Start: a quick introduction to Nimbus API and how to write and run your first simple Nimbus application.
Nimbus Programming Guide: detailed overview of Nimbus API and how to build and link applications against Nimbus core library.
Launching Nimbus Cluster: overview of the scripts to launch Nimbus controller and workers locally or over a cluster of machines.
Amazon EC2: scripts that help you launch an elastic cluster of Nimbus nodes on Amazon EC2. In addition, there are scripts for submitting, monitoring and managing the application on EC2.
Monitoring: how to monitor and profile Nimbus runtime and application performance.
Evaluation: overview of the key benchmark evaluations and instructions on how to regenerate the results.