SIMD-Optimized Search Over Sorted Data

Applications often require a fast, single-threaded search algorithm over sorted data, typical in table-lookup operations. We explore various search algorithms for a large number of search candidates over a relatively small array of logarithmically-distributed sorted data. These include an innovative hash-based search that takes advantage of floating point representation to bin data by the exponent. Algorithms that can be optimized to take advantage of SIMD vector instructions are of particular interest. We then conduct a case study applying our results and analyzing algorithmic performance with the EOSPAC package. EOSPAC is a table look-up library for manipulation and interpolation of SESAME equation-of-state data. Our investigation results in a couple of algorithms with better performance with a best case eight times speedup over the original EOSPAC Hunt-and-Locate implementation. Our techniques should generalize to other instances of search algorithms seeking to get a performance boost from vectorization.

Revisiting the functional bootstrap in TFHE

The FHEW cryptosystem introduced the idea that an arbitrary function can be evaluated within the bootstrap procedure as a table lookup. The faster bootstraps of TFHE strengthened this approach, which was later named Functional Bootstrap (Boura et al., CSCML’19). From then on, little effort has been made towards defining efficient ways of using it to implement functions with high precision. In this paper, we introduce two methods to combine multiple functional bootstraps to accelerate the evaluation of reasonably large look-up tables and highly precise functions. We thoroughly analyze and experimentally validate the error propagation in both methods, as well as in the functional bootstrap itself. We leverage the multi-value bootstrap of Carpov et al. (CT-RSA’19) to accelerate (single) lookup table evaluation, and we improve it by lowering the complexity of its error variance growth from quadratic to linear in the value of the output base. Compared to previous literature using TFHE’s functional bootstrap, our methods are up to 2.49 times faster than the lookup table evaluation of Carpov et al. (CT-RSA’19) and up to 3.19 times faster than the 32-bit integer comparison of Bourse et al. (CT-RSA’20). Compared to works using logic gates, we achieved speedups of up to 6.98, 8.74, and 3.55 times over 8-bit implementations of the functions ReLU, Addition, and Maximum, respectively.

An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.