1,329 research outputs found
Chisel: Reliability- and Accuracy-Aware Optimization of Approximate Computational Kernels
The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy.
We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification.
We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant CCF-1138967)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Dept. of Energy (Grant DE-SC0008923)United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192)United States. Defense Advanced Research Projects Agency (Grant FA8750-12-2-0110)United States. Defense Advanced Research Projects Agency (Grant FA-8750-14-2-0004
Formal change impact analyses for emulated control software
Processor emulators are a software tool for allowing legacy computer programs to be executed on a modern processor. In the past emulators have been used in trivial applications such as maintenance of video games. Now, however, processor emulation is being applied to safety-critical control systems, including military avionics. These applications demand utmost guarantees of correctness, but no verification techniques exist for proving that an emulated system preserves the original system’s functional and timing properties. Here we show how this can be done by combining concepts previously used for reasoning about real-time program compilation, coupled with an understanding of the new and old software architectures. In particular, we show how both the old and new systems can be given a common semantics, thus allowing their behaviours to be compared directly
On-stack replacement, distilled
On-stack replacement (OSR) is essential technology for adaptive optimization, allowing changes to code actively executing in a managed runtime. The engineering aspects of OSR are well-known among VM architects, with several implementations available to date. However, OSR is yet to be explored as a general means to transfer execution between related program versions, which can pave the road to unprecedented applications that stretch beyond VMs. We aim at filling this gap with a constructive and provably correct OSR framework, allowing a class of general-purpose transformation functions to yield a special-purpose replacement. We describe and evaluate an implementation of our technique in LLVM. As a novel application of OSR, we present a feasibility study on debugging of optimized code, showing how our techniques can be used to fix variables holding incorrect values at breakpoints due to optimizations
Building Efficient Query Engines in a High-Level Language
Abstraction without regret refers to the vision of using high-level
programming languages for systems development without experiencing a negative
impact on performance. A database system designed according to this vision
offers both increased productivity and high performance, instead of sacrificing
the former for the latter as is the case with existing, monolithic
implementations that are hard to maintain and extend. In this article, we
realize this vision in the domain of analytical query processing. We present
LegoBase, a query engine written in the high-level language Scala. The key
technique to regain efficiency is to apply generative programming: LegoBase
performs source-to-source compilation and optimizes the entire query engine by
converting the high-level Scala code to specialized, low-level C code. We show
how generative programming allows to easily implement a wide spectrum of
optimizations, such as introducing data partitioning or switching from a row to
a column data layout, which are difficult to achieve with existing low-level
query compilers that handle only queries. We demonstrate that sufficiently
powerful abstractions are essential for dealing with the complexity of the
optimization effort, shielding developers from compiler internals and
decoupling individual optimizations from each other. We evaluate our approach
with the TPC-H benchmark and show that: (a) With all optimizations enabled,
LegoBase significantly outperforms a commercial database and an existing query
compiler. (b) Programmers need to provide just a few hundred lines of
high-level code for implementing the optimizations, instead of complicated
low-level code that is required by existing query compilation approaches. (c)
The compilation overhead is low compared to the overall execution time, thus
making our approach usable in practice for compiling query engines
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