20910959
Comments on a paper by T.C. Chen and I.T. Ho
Tien Chi Chen and Irving T. Ho in their paper “Storage-Efficient Representation of Decimal Data” [1] present a scheme for the compression of numbers stored in binary coded decimal (BCD) form. Their compression scheme involves coding two or three digits at a time in seven bit or ten bit fields and requires only permutations, deletions, and insertions for coding and decoding. We would like to briefly present some alternatives to their compression method and to discuss the relative advantages of these other methods.
12460438
Evaluation of cache consistency algorithm performance
As more computer systems turn to multiprocessing for improved performance, additional research is needed to evaluate and improve the performance of cache consistency protocols. In this study, we use trace-driven simulation to examine the performance of several consistency protocols, including some new adaptive protocols which have not been examined in prior research. This study uses a wider variety of traces than have been previously analyzed, including some production applications from a vector mini-supercomputer system, and presents a wider variety of analyses than have been previously presented for a given workload. We find that the sharing characteristics of application programs have a large bearing on the relative performance of the different protocols. Update-based protocols outperform invalidate-based protocols when accesses so shared data are highly interleaved among different processors (fine-grain sharing), while invalidate-based protocols are superior if one processor performs all accesses to shared data over long periods of time (coarse-grain sharing). Adaptive protocols provide the best overall performance across all applications; we present a new protocol called Update-Once, which yields the highest average performance. In even the best cases, however, estimated processor utilizations are unacceptably low due to the overhead to maintain consistent caches. To extract good performance from multiprocessor systems, existing application programs must be recoded to reduce sharing between processors.
36713689
Performance Characterization of Optimizing Compilers
Optimizing compilers have become an essential component in achieving high levels of performance. Various simple and sophisticated optimizations are implemented at different stages of compilation to yield significant improvements, but little work has been done in characterizing the effectiveness of optimizers, or in understanding where most of this improvement comes from. We study the performance impact of optimization in the context of our methodology for CPU performance characterization based on the abstract machine model. The model considers all machines to be different implementations of the same high level language abstract machine; in previous research, the model has been used as a basis to analyze machine and benchmark performance. We show that our model can be extended to characterize the performance improvement provided by optimizers and to predict the run time of optimized programs, and measure the effectiveness of several compilers in implementing different optimization techniques. >
14423694
Characteristics of I/O traffic in personal computer and server workloads
Understanding the characteristics of I/O traffic is increasingly important as the performance gap between the processor and disk-based storage continues to widen. Moreover, recent advances in technology, coupled with market demands, have led to new and exciting developments in storage systems, particularly network storage, storage utilities, and intelligent self-optimizing storage. In this paper, we empirically examine the physical I/O traffic of a wide range of server and personal computer (PC) workloads, focusing on how these workloads will be affected by the recent developments in storage systems. As part of our analysis, we compare our results with historical data and re-examine some rules of thumb (e.g., one bit of I/O per second for each instruction per second of processing power) that have been widely used for designing computer systems. We find that the I/O traffic is bursty and appears to exhibit self-similar characteristics. Our analysis also indicates that there is little cross-correlation between traffic volumes of server workloads, which suggests that aggregating these workloads will likely help to smooth out the traffic and enable more efficient utilization of resources. We discover that there is significant potential for harnessing "free" system resources to perform background tasks such as optimization of disk block layout. In general, we observe that the characteristics of the I/O traffic are relatively insensitive to the extent of upstream caching, and thus our results still apply, on a qualitative level, when the upstream cache is increased in size.
11584462
The automatic improvement of locality in storage systems
Disk I/O is increasingly the performance bottleneck in computer systems despite rapidly increasing disk data transfer rates. In this article, we propose Automatic Locality-Improving Storage (ALIS), an introspective storage system that automatically reorganizes selected disk blocks based on the dynamic reference stream to increase effective storage performance. ALIS is based on the observations that sequential data fetch is far more efficient than random access, that improving seek distances produces only marginal performance improvements, and that the increasingly powerful processors and large memories in storage systems have ample capacity to reorganize the data layout and redirect the accesses so as to take advantage of rapid sequential data transfer. Using trace-driven simulation with a large set of real workloads, we demonstrate that ALIS considerably outperforms prior techniques, improving the average read performance by up to 50% for server workloads and by about 15% for personal computer workloads. We also show that the performance improvement persists as disk technology evolves. Since disk performance in practice is increasing by only about 8% per year, the benefit of ALIS may correspond to as much as several years of technological progress.
3191261
Multiprocessor memory organization and memory interference
ture of shared memory in a multiprocessor computer system is examined with particular attention to noninterleaved memory. Alternative memory organizations are compared and it is shown that a home memory organization, in which each processor is associated with one or more memories in which its address space is concentrated, is quite effective in reducing memory interference. Home memory organization is shown to be particularly suited to certain specialized computational problems as well as to possess advantages in terms of interference and reliability for general purpose computation. Results for interleaved memory are drawn from previous work and are used for comparison. Trace-driven simulations are used to verify the conclusions of the analysis.
2433417
Logarithmic Opinion Pools for Conditional Random Fields
Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation to counter the tendency of these models to overfit. The standard approach to regularising CRFs involves a prior distribution over the model parameters, typically requiring search over a hyperparameter space. In this paper we address the overfitting problem from a different perspective, by factoring the CRF distribution into a weighted product of individual "expert" CRF distributions. We call this model a logarithmic opinion pool (LOP) of CRFs (LOP-CRFs). We apply the LOP-CRF to two sequencing tasks. Our results show that unregularised expert CRFs with an unregularised CRF under a LOP can outperform the unregularised CRF, and attain a performance level close to the regularised CRF. LOP-CRFs therefore provide a viable alternative to CRF regularisation without the need for hyperparameter search.
840255
Scaling Conditional Random Fields Using Error-Correcting Codes
Conditional Random Fields (CRFs) have been applied with considerable success to a number of natural language processing tasks. However, these tasks have mostly involved very small label sets. When deployed on tasks with larger label sets, the requirements for computational resources mean that training becomes intractable.This paper describes a method for training CRFs on such tasks, using error correcting output codes (ECOC). A number of CRFs are independently trained on the separate binary labelling tasks of distinguishing between a subset of the labels and its complement. During decoding, these models are combined to produce a predicted label sequence which is resilient to errors by individual models.Error-correcting CRF training is much less resource intensive and has a much faster training time than a standardly formulated CRF, while decoding performance remains quite comparable. This allows us to scale CRFs to previously impossible tasks, as demonstrated by our experiments with large label sets.
18711767
Semantic Generalisation and the Inference of Meaning
In this paper, a computational model of a successful negotiated communication system is presented, in which language agents develop their own meanings in response to their environment and attempt to infer the meanings of others’ utterances. The inherent uncertainty in the process of meaning inference in the system leads to variation in the agents’ internal semantic representations, which then itself drives language change in the form of semantic generalisation.
37361807
Robust Fuzzy Extractors and Authenticated Key Agreement from Close Secrets
Consider two parties holding correlated random variables W and W′, respectively, that are within distance t of each other in some metric space. These parties wish to agree on a uniformly distributed secret key R by sending a single message over an insecure channel controlled by an all-powerful adversary. We consider both the keyless case, where the parties share no additional secret information, and the keyed case, where the parties share a long-term secret SK that they can use to generate a sequence of session keys {Rj} using multiple pairs {(Wj, W′j)}. The former has applications to, e.g., biometric authentication, while the latter arises in, e.g., the bounded storage model with errors. 
 
Our results improve upon previous work in several respects: 
 
– The best previous solution for the keyless case with no errors (i.e., t=0) requires the min-entropy of W to exceed 2|W|/3. We show a solution when the min-entropy of W exceeds the minimal threshold |W|/2. 
 
– Previous solutions for the keyless case in the presence of errors (i.e., t>0) required random oracles. We give the first constructions (for certain metrics) in the standard model. 
 
– Previous solutions for the keyed case were stateful. We give the first stateless solution.
5725147
Local search genetic algorithm for optimal design of reliable networks
This paper presents a genetic algorithm (GA) with specialized encoding, initialization, and local search operators to optimize the design of communication network topologies. This NP-hard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible networks. Another complication is that the fitness function involves calculating the all-terminal reliability of the network, which is a computationally expensive calculation. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This strategy of employing bounds, simple heuristic checks, and problem-specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive.