In recent years there has been a proliferation of parallel and heterogeneous architectures. As chip designers have hit fundamental limits in traditional processor scaling, they have begun rethinking processor architecture from the ground up. In addition to creating new classes of processors, chip designers have revisited CPU microarchitecture in order to target different computing contexts. CPUs have been optimized for low-power smartphones and extended for high-performance computing in order to achieve better performance energy efficiency for heavy computational tasks. Although heterogeneity adds significant complexity to both hardware and software, recent works have shown tremendous power and performance benefits obtainable through specialization. It is clear that emerging systems will be increasingly heterogeneous.

Many of these emerging systems couple together cores of different instruction set architectures (ISA), due to both market forces and the potential performance and power benefits in optimizing application execution. However, differently from symmetric multiprocessors or even asymmetric single-ISA multiprocessors, natively compiled applications cannot freely migrate between heterogeneous-ISA processors. This is due to the fact that applications are compiled to an instruction set architecture-specific format which is incompatible on other instruction set architectures. This creates serious limitations, as execution migration is a fundamental mechanism used by schedulers to reach performance or fairness goals, allows applications to migrate between heterogeneous-ISA CPUs in order to accelerate parallel applications or even leverage ISA-heterogeneity for security benefits.

This dissertation describes system software for automatically migrating natively compiled applications across heterogeneous-ISA processors. This dissertation describes the implementation and evaluation of a complete software stack on commodity scale heterogeneous-ISA CPUs, emulating datacenters with heterogeneous-ISA systems or future systems that tightly integrate heterogeneous-ISA CPUs via point-to-point interconnect. This dissertation describes a compiler which builds applications for heterogeneous-ISA execution migration. The compiler generates machine code for every architecture in the system and lays out the application's code and data in a common format. In addition, the compiler generates metadata used by a state transformation runtime to dynamically transform thread execution state between ISA-specific formats, allowing application threads to migrate between different ISAs.

The compiler and runtime is evaluated in conjunction with a replicated-kernel operating system, which provides thread migration and distributed shared virtual memory across heterogeneous-ISA processors. This redesigned software stack is evaluated on a setup containing and ARM and an x86 processor interconnected via point-to-point interconnect over PCIe. This dissertation shows that sub-millisecond state transformation is achievable. Additionally, it shows that for a datacenter-like workload using benchmarks from the NAS Parallel Benchmark suite, the system can trade some performance for up to a 66% reduction in energy and up to an 11% reduction in energy-delay product.

This dissertation then describes an exploration into using hardware transactional memory (HTM) to maximize scheduling flexibility. Because applications can only migrate between ISAs at program locations with specific properties, there may be a significant delay between when the scheduler wishes to migrate an application and when the application can respond to the migration request. In order to reduce this migration response time, this dissertation describes compiler instrumentation which uses HTM to allow the scheduler to force applications to roll back to the most recently encountered program location suitable for migration. This is evaluated both in terms of overhead and responsiveness to migration requests.

In addition to showing the viability of the infrastructure for optimizing workload placement in a heterogeneous-ISA datacenter, this dissertation also demonstrates utilizing the infrastructure to accelerate multithreaded applications. This dissertation describes a new OpenMP runtime named libopenpop that is optimized for executing applications in heterogeneous- ISA systems with distributed shared virtual memory. The runtime utilizes synchronization primitives that enable scale-out execution across rack-scale systems and new work distribution mechanisms that predict the best partitioning of parallel work across CPUs with diverse architectural characteristics. libopenpop demonstrates sizable improvements over a na¨ıve OpenMP implementation – a 38x improvement in multi-server barrier latency, a 5.4x improvement in multi-server data reductions and a geometric mean speedup of 4.04x for scalable applications in an 8-node x86-64 cluster. For a heterogeneous system composed of a highly-clocked x86 server and a highly-parallel ARM server, libopenpop delivers up to a 4.7x speedup and a geometric mean speedup of 41% across benchmarks from several benchmark suites versus the best single-node homogeneous execution.

Finally, this dissertation describes leveraging the compiler and state transformation runtime to provide enhanced security for applications. Because the compiler provides detailed information about the stack layout of applications, it can be leveraged to defend against exploits such as stack smashing attacks and return-oriented programming attacks. This dissertation describes Chameleon, a runtime which uses the compiler and state transformation infrastructure to continuously re-randomize the stack layout and code of vulnerable applications to thwart attackers. Chameleon attaches to applications using existing operating system interfaces and periodically switches the application to new randomized stack layouts and code by rewriting the stack. Chameleon enhances security with little overhead – it disrupts a geometric mean 76.32% of code gadgets in benchmark binaries, randomizes stack element locations with geometric mean 3 potential randomized locations, and has 1.1% overhead when re-randomizing every 50 milliseconds, making it extremely difficult for attackers to exploit target applications.