Despite the great potential and flexibility of smart contract-enabled blockchains, building privacy-preserving applications using these platforms remains an open question. Existing solutions fall short since they ask end users to coordinate and perform the computation off-chain themselves. While such an approach reduces the burden of the miners of the system, it largely limits the ability of lightweight users to enjoy privacy since performing the actual computation on their own and attesting to its correctness is expensive even with state-of-the-art proof systems.To address this limitation, we propose smartFHE, a framework to support private smart contracts using fully homomorphic encryption (FHE). To the best of our knowledge, smartFHE is the first to use FHE in the blockchain model; moreover, it is the first to support arbitrary privacy-preserving applications for lightweight users under the same computation-on-demand model pioneered by Ethereum. smartFHE does not overload the user since miners are instead responsible for performing the private computation. This is achieved by employing FHE so miners can compute over encrypted data and account balances. Users are only responsible for proving well-formedness of their private inputs using efficient zero-knowledge proof systems (ZKPs). We formulate a notion for a privacy-preserving smart contract (PPSC) scheme and show a concrete instantiation of our smartFHE framework. We address challenges resulting from using FHE in the blockchain setting—including concurrency and dealing with leveled schemes. We also show how to choose suitable FHE and ZKP schemes to instantiate our framework, since naively choosing these will lead to poor performance in practice. We formally prove correctness and security of our construction. Finally, we conduct experiments to evaluate its efficiency, including comparisons with a state-of-the-art scheme and testing several private smart contract applications. We have open-sourced our (highly optimized) ZKP library, which could be of independent interest.

SUMMARY Graphics processing units and multicore processors are now pervasive in computational sciences and high‐performance computing. Their high arithmetic throughput and memory bandwidth combined with their ever increasing programmability make them suitable for a widening variety of applications. We give a high level overview of Specmaster, a Myrimatch port that can use every open computing language device available in a machine to identify peptides in tandem mass spectrometry data. We then highlight device‐specific optimizations for multi‐core CPUs and graphics processing units as well as describe our framework for implementing these optimizations while still using a single code base. We also provide performance results of Specmaster running on four different architectures and compare these numbers with Myrimatch. Finally, we improve on our existing work by showing Specmaster dynamically load‐balancing on 3 Radeon 7970s and 32 Interlagos 6272 cores as well as comparing the quality of our search results with Myrimatch. Copyright © 2013 John Wiley & Sons, Ltd.

Multicore processors and a variety of accelerators have allowed scientific applications to scale to larger problem sizes. We present a performance, design methodology, platform, and architectural comparison of several application accelerators executing a Quantum Monte Carlo application. We compare the application's performance and programmability on a variety of platforms including CUDA with Nvidia GPUs, Brook+ with ATI graphics accelerators, OpenCL running on both multicore and graphics processors, C++ running on multicore processors, and a VHDL implementation running on a Xilinx FPGA. We show that OpenCL provides application portability between multicore processors and GPUs, but may incur a performance cost. Furthermore, we illustrate that graphics accelerators can make simulations involving large numbers of particles feasible.

Background While multiple tools exist for the analysis and identification of spectra generated in shotgun proteomics experiments, few easily implemented tools exist that allow for the automated analysis of the quality of spectra. A researcher’s knowledge of the quality of a spectra from an experiment can be helpful in determining possible reasons for misidentification or lack of identification of spectra in a sample.

Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.