HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Extending the PCIe Interface with Parallel Compression/Decompression Hardware for Energy and Performance Optimization

PCIe is a high-performing interface used to move data from a central host PC to an accelerator such as Field Programmable Gate Arrays (FPGA). This interface allows a system to perform fast data transfers in High-Performance Computing (HPC) and provide a performance boost. However, HPC systems normally require large datasets, and in these situations PCIe can become a bottleneck. To address this issue, we propose an open-source hardware compression/decompression system that can be used to adapt with continuously-streamed data with low latency and high throughput. We implement a compressor and decompressor engines on FPGA, scale up with multiple engines working in parallel, and evaluate the energy reduction and performance with different numbers of multiple engines. To alleviate the performance bottleneck in the processor acting as a controller, we propose a hardware scheduler to fairly distribute the datasets among the engines. Our design reduces the transmission time in PCIe, and the results show an energy reduction of up to 48% in the PCIe transfers, thanks to the decrease in the number of bits that have to be transmitted. The overhead in terms of latency is maintained to a minimum and user selectable depending on the tolerances of the intended application

High performance in the cloud with FPGA groups

International audienceField-programmable gate arrays (FPGAs) can offer invaluable computational performance for many compute-intensive algorithms. However, to justify their purchase and administration costs it is necessary to maximize resource utilization over their expected lifetime. Making FPGAs available in a cloud environment would make them attractive to new types of users and applications and help democratize this increasingly popular technology. However, there currently exists no satisfactory technique for offering FPGAs as cloud resources and sharing them between multiple tenants. We propose FPGA groups, which are seen by their clients as a single virtual FPGA, and which aggregate the computational power of multiple physical FPGAs. FPGA groups are elastic, and they may be shared among multiple tenants. We present an autoscaling algorithm to maximize FPGA groups' resource utilization and reduce user-perceived computation latencies. FPGA groups incur a low overhead in the order of 0.09ms per submitted task. When faced with a challenging workload, the autoscaling algorithm increases resource utilization from 52% to 61% compared to a static resource allocation, while reducing task execution latencies by 61%