Resource disaggregation is prevalent in datacenters since it provides high resource utilization when compared to servers dedicated to either compute, memory, or storage. NVMe-over-Fabrics (NVMe-oF) is the standardized protocol for accessing disaggregated network storage. Currently, the NVMe-oF specification lacks semantics to prioritize I/O requests based on different application needs. Since applications have varying goals — latency-sensitive or throughput-critical I/O — we need to design efficient schemes to allow applications to specify the type of performance they wish to achieve. To this end, we propose a new NVMe-over-Priority-Fabrics (NVMe-oPF) protocol with multi-tenancy support that allows applications to specify whether to optimize for latency or throughput. NVMe-oPF proposes coalescing request completions, lock-free optimization, zero-copy queues, out-of-order request completion handling, and window size optimization for the specific I/O patterns, queue depths, and I/O sizes that yield the best performance. Our NVMe-oPF-10Gbps can achieve up to 2.94X improvement in throughput and reduces tail latency by up to 32.1% for highly concurrent multi-tenant read workloads when compared to the state-of-the-art userspace NVMe-oF runtime design in Intel Storage Performance Development Kit (SPDK). For write workloads with 100Gbps, NVMe-oPF achieves a 32.6% increase in throughput while maintaining low latency compared to SPDK. We also bring performance benefits to the application level with HDF5 by increasing write workload throughput by 25.2% in larger-scale experiments.

The amount of data being generated in genomics is predicted to be between 2 and 40 exabytes per year for the next decade, making genomic analysis the new frontier and the new challenge for precision medicine. This paper explores targeted deployment of hardware accelerators in the cloud to improve the runtime and throughput of immensescale genomic data analyses. In particular, INDEL (INsertion/DELetion) realignment is a critical operation that enables diagnostic testings of cancer through error correction prior to variant calling. It is the slowest part of the somatic (cancer) genomic analysis pipeline, the alignment refinement pipeline, and represents roughly one-third of the execution time of timesensitive diagnostics for acute cancer patients. To accelerate genomic analysis, this paper describes a hardware accelerator for INDEL realignment (IR), and a hardware-software framework leveraging FPGAs-as-a-service in the cloud. We chose to implement genomics analytics on FPGAs because genomic algorithms are still rapidly evolving (e.g. the de facto standard “GATK Best Practices” has had five releases since January of this year). We chose to deploy genomics accelerators in the cloud to reduce capital expenditure and to provide a more quantitative performance and cost analysis. We built and deployed a sea of IR accelerators using our hardware-software accelerator development framework on AWS EC2 F1 instances. We show that our IR accelerator system performed 81× better than multi-threaded genomic analysis software while being 32× more cost efficient.