The Dynamic Execution Layer Interface (DELI) offers the following unique capability: it provides fine-grain control over the execution of programs, by allowing its clients to observe and optionally manipulate every single instruction-at run time-just before it runs. DELI accomplishes this by opening up an interface to the layer between the execution of software and hardware. To avoid the slowdown, DELI caches a private copy of the executed code and always runs out of its own private cache.In addition to giving powerful control to clients, DELI opens up caching and linking to ordinary emulators and just-in-time compilers, which then get the reuse benefits of the same mechanism. For example, emulators themselves can also use other clients, to mix emulation with already existing services, native code, and other emulators. This paper describes the basic aspects of DELI, including the underlying caching and linking mechanism, the Hardware Abstraction Mechanism (HAM), the BinaryLevel Translation (BLT) infrastructure, and the Application Programming Interface (API) exposed to the clients. We also cover some of the services that clients could offer through the DELI, such as ISA emulation, software patching, and sandboxing. Finally, we consider a case study of emulation in detail: the emulation of a PocketPC system on the Lx/ST210 embedded VLIW processor. In this case, DELI enables us to achieve near-native performance, and to mix-and-match native and emulated code.

Abstract-Cloud computing is emerging as an alternative to supercomputers for some of the high-performance computing (HPC) applications that do not require a fully dedicated machine. With cloud as an additional deployment option, HPC users are faced with the challenges of dealing with highly heterogeneous resources, where the variability spans across a wide range of processor configurations, interconnections, virtualization environments, and pricing rates and models.In this paper, we take a holistic viewpoint to answer the question -why and who should choose cloud for HPC, for what applications, and how should cloud be used for HPC? To this end, we perform a comprehensive performance evaluation and analysis of a set of benchmarks and complex HPC applications on a range of platforms, varying from supercomputers to clouds. Further, we demonstrate HPC performance improvements in cloud using alternative lightweight virtualization mechanisms -thin VMs and OS-level containers, and hypervisor-and application-level CPU affinity. Next, we analyze the economic aspects and business models for HPC in clouds. We believe that is an important area that has not been sufficiently addressed by past research. Overall results indicate that current public clouds are cost-effective only at small scale for the chosen HPC applications, when considered in isolation, but can complement supercomputers using business models such as cloud burst and application-aware mapping.

Nearly all personal computer and workstation processors, and virtually all high-performance embedded processor cores, now embody instruction level parallel (ILP)

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In this paper we report on a system which automatically designs realistic VLIW architectures highly optimized for one given application (the input for this system), while running all other code correctly. The system uses a productquality compiler that generates very aggressive VLJW code. We retarget the compiler until we have found a VLJW architecture idealized for the application on the basis of performance, a cost function and a hardware budget.We show that we can automatically select architectures that achieve large speedups on color and image processing codes. Specialization is shown to be very valuable: The differences between architectural choices, even among reasonable-seeming architectures having similar costs, can be very great, often a factor of 5 (andsometimes much more). We show also that specialization is also very dangerous. A reasonable choice of architecture to fit one algorithm can be a very poor choice for anothel; even in the same domain.There is sometimes an architecture, near in cost and performance to the best, that does much better on a second algorithm.

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