Fast Table Lookup Research Articles

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. We develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

High‐order Taylor series approximation for efficient computation of elementary functions

A new piecewise polynomial method is proposed to compute elementary functions by using high-order Taylor approximation. The high-order power terms of the series are proposed to be approximated by using simple and fast table lookup. Furthermore, the similarity and regularity among the Taylor coefficients can make possible the sharing of the lookup tables. The authors have developed an error analysis method to estimate the maximum error of the proposed approximation approach, and formulated the procedure for determining the hardware parameters in the approximation unit. Finally, the authors have designed a single-precision approximation unit for computing six common elementary functions. The coefficient sharing approach can result in at least 30.5% reduction in the coefficient lookup tables. Compared with a previous work by Pineiro et al., the authors can save 27.91% of the lookup tables with some extra cost in computation hardware. Compared with the work by Alimohammad et al., 34.85% of the lookup tables can be saved with the same computation hardware cost. The authors conclude that the proposed approaches can effectively reduce the lookup tables required in the piecewise polynomial approximation for efficient elementary function computation.

Distributed Database Kriging for Adaptive Sampling ([formula omitted

We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5–25, while retaining high accuracy for various choices of the algorithm parameters.

Accurate and fast table look-up models for leakage current analysis in 65 nm CMOS technology

Novel physical models for leakage current analysis in 65 nm technology are proposed. Taking into consideration the process variations and emerging effects in nano-scaled technology, the presented models are capable of accurately estimating the subthreshold leakage current and junction tunneling leakage current in 65 nm technology. Based on the physical models, new table look-up models are developed and first applied to leakage current analysis in pursuit of higher simulation speed. Simulation results show that the novel physical models are in excellent agreement with the data measured from the foundry in the 65 nm process, and the proposed table look-up models can provide great computational efficiency by using suitable interpolation techniques. Compared with the traditional physical-based models, the table look-up models can achieve 2.5X speedup on average on a variety of industry circuits.

A Simple Technique for Efficient Computation of Electromagnetic Coupling in Microwave Integrated Circuits

Because of ever-higher operating frequencies and circuit integration, electromag- netic (EM) coupling efiects are becoming increasingly important in microwave integrated circuits. Existing EM-based numerical methods can generate accurate models for passives but sufier from high computation time and memory requirements, while fast table look-up models and equivalent circuit models do not adequately include distributed EM coupling between adjacent components. Using simple network theory principles and de-embedding concepts, the proposed technique al- lows fast and e-cient computation of such coupling, making the design more reliable.

EaseCAM: An Energy and Storage Efficient TCAM-Based Router Architecture for IP Lookup

Ternary content addressable memories (TCAMs) have been emerging as a popular device in designing routers for packet forwarding and classifications. Despite their premise on high-throughput, large TCAM arrays are prohibitive due to their excessive power consumption and lack of scalable design schemes. We present a TCAM-based router architecture that is energy and storage efficient. We introduce prefix aggregation and expansion techniques to compact the effective TCAM size in a router. Pipelined and paging schemes are employed in the architecture to activate a limited number of entries in the TCAM array during an IP lookup. The new architecture provides low power, fast incremental updating, and fast table look-up. Heuristic algorithms for page filling, fast prefix update, and memory management are also provided. Results have been illustrated with two large routers (bbnplanet and attcanada) to demonstrate the effectiveness of our approach.

Hierarchical vector quantisation

We present a method of vector quantisation which trades off accuracy for speed of encoding. We achieve this by hierarchically structuring a multistage encoder so that each stage encodes low dimensional input vectors. Such hierarchical encoders may easily be realised as a set of fast table look-up operations. We demonstrate how the Euclidean distortion in such a multistage encoder is approximately minimised by using Kohonen's topographic mapping learning algorithm from neural network theory. We also demonstrate the performance of the technique on various stochastic time series. We find that there is little loss in encoding accuracy, when compared with the exact nearest neighbour encoding using an equivalent single stage encoder.