5777300
Evaluating Associativity in CPU Caches
The authors present new and efficient algorithms for simulating alternative direct-mapped and set-associative caches and use them to quantify the effect of limited associativity on the cache miss ratio. They introduce an algorithm, forest simulation, for simulating alternative direct-mapped caches and generalize one, which they call all-associativity simulation, for simulating alternative direct-mapped, set-associative, and fully-associative caches. The authors find that although all-associativity simulation is theoretically less efficient than forest simulation or stack simulation (a commonly used simulation algorithm), in practice it is not much slower and allows the simulation of many more caches with a single pass through an address trace. The authors also provide data and insight into how varying associatively affects the miss ratio. >
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. >
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.
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.
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.
13460881
Multi-objective tabu search using a multinomial probability mass function
A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible.
1672422
Entropic Security and the Encryption of High Entropy Messages
We study entropic security, an information-theoretic notion of security introduced by Russell and Wang [24] in the context of encryption and by Canetti et al. [5,6] in the context of hash functions. Informally, a probabilitic map $Y = \mathcal{E}(X)$ (e.g., an encryption sheme or a hash function) is entropically secure if knowledge of Y does not help predicting any predicate of X, whenever X has high min-entropy from the adversary's point of view. On one hand, we strengthen the formulation of [5,6,24] and show that entropic security in fact implies that Y does not help predicting any function of X (as opposed to a predicate), bringing this notion closer to the conventioonal notion of semantic security [10]. On the other hand, we also show that entropic security is equivalent to indistinguishability on pairs of input distributions of sufficiently high entropy, which is in turn related to randomness extraction from non-uniform distributions [21]. 
 
We then use the equivalence above, and the connection to randomness extraction, to prove several new results on entropically-secure encryption. First, we give two general frameworks for constructing entropically secure encryption schemes: one based on expander graphs and the other on XOR-universal hash functions. These schemes generalize the schemes of Russell and Wang, yielding simpler constructions and proofs, as well as improved parameters. To encrypt an n-bit message of min-entropy t while allowing at most e-advantage to the adversary, our best schemes use a shared secret key of length $k = n - t + 2{\rm log} (\frac {1}{\epsilon})$. Second, we obtain lower bounds on the key length k for entropic security and indistinguishability. In particular, we show near tightness of our constructions: k > n – t. For a large class of schemes — including all the schemes we study — the bound can be strengthened to $k \geq n - t+{\rm log} (\frac {1}{\epsilon})-O(1)$.
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.
519042
Evolving a Scalable Multirobot Controller Using an Artificial Neural Tissue Paradigm
We present an "artificial neural tissue" (ANT) architecture as a control system for autonomous multirobot tasks. This architecture combines a typical neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue which in turn allows such emergent capabilities as task decomposition. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to derive controllers for the task in simulation. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT-based controllers are shown to exhibit self-organization, employ stigmergy and make use of templates (unlabeled environmental cues). These controllers have been tested on a multirobot resource-collection task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings. The issues of scalability and antagonism are addressed
16194916
Cross-Situational Learning: A Mathematical Approach
We present a mathematical model of cross-situational learning, in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further note that even large vocabularies are learnable through cross-situational learning.