Genesis: A Hardware Acceleration Framework for Genomic Data Analysis

2020 · 23 citations

• Computer Science
• Computer Science
• Computer architecture

In this paper, we describe our vision to accelerate algorithms in the domain of genomic data analysis by proposing a framework called Genesis (genome analysis) that contains an interface and an implementation of a system that processes genomic data efficiently. This framework can be deployed in the cloud and exploit the FPGAs-as-a-service paradigm to provide cost-efficient secondary DNA analysis. We propose conceptualizing genomic reads and associated read attributes as a very large relational database and using extended SQL as a domain-specific language to construct queries that form various data manipulation operations. To accelerate such queries, we design a Genesis hardware library which consists of primitive hardware modules that can be composed to construct a dataflow architecture specialized for those queries. As a proof of concept for the Genesis framework, we present the architecture and the hardware implementation of several genomic analysis stages in the secondary analysis pipeline corresponding to the best known software analysis toolkit, GATK4 workflow proposed by the Broad Institute. We walk through the construction of genomic data analysis operations using a sequence of SQL-style queries and show how Genesis hardware library modules can be utilized to construct the hardware pipelines designed to accelerate such queries. We exploit parallelism and data reuse by utilizing a dataflow architecture along with the use of on-chip scratchpads as well as non-blocking APIs to manage the accelerators, allowing concurrent execution of the accelerator and the host. Our accelerated system deployed on the cloud FPGA performs up to 19.3× better than GATK4 running on a commodity multi-core Xeon server and obtains up to 15× better cost savings. We believe that if a software algorithm can be mapped onto a hardware library to utilize the underlying accelerator(s) using an already-standardized software interface such as SQL, while allowing the efficient mapping of such interface to primitive hardware modules as we have demonstrated here, it will expedite the acceleration of domainspecific algorithms and allow the easy adaptation of algorithm changes.

Keystone

2020 · 358 citations

• Computer Science
• Computer Science
• Computer Security

Trusted execution environments (TEEs) see rising use in devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone---the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and flexible feature choices. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models.