16,166 research outputs found

    VXA: A Virtual Architecture for Durable Compressed Archives

    Full text link
    Data compression algorithms change frequently, and obsolete decoders do not always run on new hardware and operating systems, threatening the long-term usability of content archived using those algorithms. Re-encoding content into new formats is cumbersome, and highly undesirable when lossy compression is involved. Processor architectures, in contrast, have remained comparatively stable over recent decades. VXA, an archival storage system designed around this observation, archives executable decoders along with the encoded content it stores. VXA decoders run in a specialized virtual machine that implements an OS-independent execution environment based on the standard x86 architecture. The VXA virtual machine strictly limits access to host system services, making decoders safe to run even if an archive contains malicious code. VXA's adoption of a "native" processor architecture instead of type-safe language technology allows reuse of existing "hand-optimized" decoders in C and assembly language, and permits decoders access to performance-enhancing architecture features such as vector processing instructions. The performance cost of VXA's virtualization is typically less than 15% compared with the same decoders running natively. The storage cost of archived decoders, typically 30-130KB each, can be amortized across many archived files sharing the same compression method.Comment: 14 pages, 7 figures, 2 table

    Six networks on a universal neuromorphic computing substrate

    Get PDF
    In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality

    Fast and Correct Load-Link/Store-Conditional Instruction Handling in DBT Systems

    Get PDF
    Dynamic Binary Translation (DBT) requires the implementation of load-link/store-conditional (LL/SC) primitives for guest systems that rely on this form of synchronization. When targeting e.g. x86 host systems, LL/SC guest instructions are typically emulated using atomic Compare-and-Swap (CAS) instructions on the host. Whilst this direct mapping is efficient, this approach is problematic due to subtle differences between LL/SC and CAS semantics. In this paper, we demonstrate that this is a real problem, and we provide code examples that fail to execute correctly on QEMU and a commercial DBT system, which both use the CAS approach to LL/SC emulation. We then develop two novel and provably correct LL/SC emulation schemes: (1) A purely software based scheme, which uses the DBT system’s page translation cache for correctly selecting between fast, but unsynchronized, and slow, but fully synchronized memory accesses, and (2) a hardware accelerated scheme that leverages hardware transactional memory (HTM) provided by the host. We have implemented these two schemes in the Synopsys DesignWare® ARC® nSIM DBT system, and we evaluate our implementations against full applications, and targeted micro-benchmarks. We demonstrate that our novel schemes are not only correct, but also deliver competitive performance on-par or better than the widely used, but broken CAS scheme.Postprin

    Hardware-accelerated interactive data visualization for neuroscience in Python.

    Get PDF
    Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization
    corecore