RFaaS: RDMA-Enabled FaaS Platform for Serverless High-Performance Computing

The rigid MPI programming model and batch scheduling dominate high-performance computing. While clouds brought new levels of elasticity into the world of computing, supercomputers still suffer from low resource utilization rates. To enhance supercomputing clusters with the benefits of serverless computing, a modern cloud programming paradigm for pay-as-you-go execution of stateless functions, we present rFaaS, the first RDMA-aware Function-as-a-Service (FaaS) platform. With hot invocations and decentralized function placement, we overcome the major performance limitations of FaaS systems and provide low-latency remote invocations in multi-tenant environments. We evaluate the new serverless system through a series of microbenchmarks and show that remote functions execute with negligible performance overheads. We demonstrate how serverless computing can bring elastic resource management into MPI-based high-performance applications. Overall, our results show that MPI applications can benefit from modern cloud programming paradigms to guarantee high performance at lower resource costs

Evaluating Component Assembly Specialization for 3D FFT

The Fast Fourier Transform (FFT) is a widely-used building block for many high-performance scienti c applications. Ef- cient computing of FFT is paramount for the performance of these applications. This has led to many e orts to implement machine and computation speci c optimizations. However, no existing FFT library is capable of easily integrating and au- tomating the selection of new and/or unique optimizations. To ease FFT specialization, this paper evaluates the use of component-based software engineering, a programming paradigm which consists in building applications by assembling small software units. Component models are known to have many software engineering bene ts but usually have insucient performance for high-performance scienti c applications. This paper uses the L2C model, a general purpose high-performance component model, and studies its performance and adaptation capabilities on 3D FFTs. Experiments show that L2C, and components in general, enables easy handling of 3D FFT specializations while obtaining performance comparable to that of well-known libraries. However, a higher-level component model is needed to automatically generate an adequate L2C assembly

M2: Malleable Metal as a Service

Existing bare-metal cloud services that provide users with physical nodes have a number of serious disadvantage over their virtual alternatives, including slow provisioning times, difficulty for users to release nodes and then reuse them to handle changes in demand, and poor tolerance to failures. We introduce M2, a bare-metal cloud service that uses network-mounted boot drives to overcome these disadvantages. We describe the architecture and implementation of M2 and compare its agility, scalability, and performance to existing systems. We show that M2 can reduce provisioning time by over 50% while offering richer functionality, and comparable run-time performance with respect to tools that provision images into local disks. M2 is open source and available at https://github.com/CCI-MOC/ims.Comment: IEEE International Conference on Cloud Engineering 201