Efficient Dual-ISA Support in a Retargetable, Asynchronous Dynamic Binary Translator

Dynamic Binary Translation (DBT) allows software compiled for one Instruction Set Architecture (ISA) to be executed on a processor supporting a different ISA. Some modern DBT systems decouple their main execution loop from the built-inJust-In-Time (JIT) compiler, i.e. the JIT compiler can operate asynchronously in a different thread without blocking program execution. However, this creates a problem for target architectures with dual-ISA support such as ARM/THUMB, where the ISA of the currently executed instruction stream may be different to the one processed by the JIT compiler due to their decoupled operation and dynamic mode changes. In this paper we present a new approach for dual-ISA support in such an asynchronous DBT system, which integrates ISA mode tracking and hot-swapping of software instruction decoders. We demonstrate how this can be achieved in a retargetable DBT system, where the target ISA is not hard-coded, but a processor-specific module is generated from a high-level architecture description. We have implemented ARM V5T support in our DBT and demonstrate execution rates of up to 1148 MIPS for the SPEC CPU 2006 benchmarks compiled for ARM/THUMB, achieving on average 192%, and up to 323%, of the speed of QEMU, which has been subject to intensive manual performance tuning and requires significant low-level effort for retargeting

A Retargetable System-Level DBT Hypervisor

System-level Dynamic Binary Translation (DBT) provides the capability to boot an Operating System (OS) and execute programs compiled for an Instruction Set Architecture (ISA) different to that of the host machine. Due to their performance critical nature, system-level DBT frameworks are typically hand-coded and heavily optimized, both for their guest and host architectures. While this results in good performance of the DBT system, engineering costs for supporting a new, or extending an existing architecture are high. In this paper we develop a novel, retargetable DBT hypervisor, which includes guest specific modules generated from high-level guest machine specifications. Our system simplifies retargeting of the DBT, but it also delivers performance levels in excess of existing manually created DBT solutions. We achieve this by combining offline and online optimizations, and exploiting the freedom of a Just-in-time (JIT) compiler operating in a bare-metal environment provided by a Virtual Machine (VM) hypervisor. We evaluate our DBT using both targeted micro-benchmarks as well as standard application benchmarks, and we demonstrate its ability to outperform the de-facto standard QEMU DBT system. Our system delivers an average speedup of 2.21× over QEMU across SPEC CPU2006 integer benchmarks running in a full-system Linux OS environment, compiled for the 64-bit ARMv8-A ISA and hosted on an x86-64 platform. For floating-point applications the speedup is even higher, reaching 6.49× on average. We demonstrate that our system-level DBT system significantly reduces the effort required to support a new ISA, while delivering outstanding performance.Publisher PD

Navigating the Landscape for Real-time Localisation and Mapping for Robotics, Virtual and Augmented Reality

Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge. Often referred to as SLAM (Simultaneous Localisation and Mapping), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are (1) tools and methodology for systematic quantitative evaluation of SLAM algorithms, (2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives, (3) end-to-end simulation tools to enable optimisation of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches, and (4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.Comment: Proceedings of the IEEE 201