Figure 1: Interactive furniture layout. For a given layout (left), our system suggests new layouts (middle) that respect the user's constraints and follow interior design guidelines. The red chair has been fixed in place by the user. One of the suggestions is shown on the right. AbstractWe present an interactive furniture layout system that assists users by suggesting furniture arrangements that are based on interior design guidelines. Our system incorporates the layout guidelines as terms in a density function and generates layout suggestions by rapidly sampling the density function using a hardware-accelerated Monte Carlo sampler. Our results demonstrate that the suggestion generation functionality measurably increases the quality of furniture arrangements produced by participants with no prior training in interior design.

We formulate the loop-free binary superoptimization task as a stochastic search problem. The competing constraints of transformation correctness and performance improvement are encoded as terms in a cost function, and a Markov Chain Monte Carlo sampler is used to rapidly explore the space of all possible programs to find one that is an optimization of a given target program. Although our method sacrifices completeness, the scope of programs we are able to consider, and the resulting quality of the programs that we produce, far exceed those of existing superoptimizers. Beginning from binaries compiled by llvm -O0 for 64-bit x86, our prototype implementation, STOKE, is able to produce programs which either match or outperform the code produced by gcc -O3, icc -O3, and in some cases, expert handwritten assembly.

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We formulate the loop-free binary superoptimization task as a stochastic search problem. The competing constraints of transformation correctness and performance improvement are encoded as terms in a cost function, and a Markov Chain Monte Carlo sampler is used to rapidly explore the space of all possible programs to find one that is an optimization of a given target program. Although our method sacrifices completeness, the scope of programs we are able to consider, and the resulting quality of the programs that we produce, far exceed those of existing superoptimizers. Beginning from binaries compiled by llvm -O0 for 64-bit x86, our prototype implementation, STOKE, is able to produce programs which either match or outperform the code produced by gcc -O3, icc -O3, and in some cases, expert handwritten assembly.

The x86-64 ISA sits at the bottom of the software stack of most desktop and server software. Because of its importance, many software analysis and verification tools depend, either explicitly or implicitly, on correct modeling of the semantics of x86-64 instructions. However, formal semantics for the x86-64 ISA are difficult to obtain and often written manually through great effort. We describe an automatically synthesized formal semantics of the input/output behavior for a large fraction of the x86-64 Haswell ISA’s many thousands of instruction variants. The key to our results is stratified synthesis, where we use a set of instructions whose semantics are known to synthesize the semantics of additional instructions whose semantics are unknown. As the set of formally described instructions increases, the synthesis vocabulary expands, making it possible to synthesize the semantics of increasingly complex instructions. Using this technique we automatically synthesized formal semantics for 1,795 instruction variants of the x86-64 Haswell ISA. We evaluate the learned semantics against manually written semantics (where available) and find that they are formally equivalent with the exception of 50 instructions, where the manually written semantics contain an error. We further find the learned formulas to be largely as precise as manually written ones and of similar size.