Performance evaluation of contemporary processors is becoming increasingly difficult due to the lack of proper frameworks. Traditionally, cycle-accurate simulators have been extensively used due to their inherent accuracy and flexibility. However, the effort involved in building them, their slow speed, and their limited ability to provide insight often imposes constraints on the extent of design exploration. In this paper, we refine our earlier Monte Carlo based CPI prediction model (Srinivasan et al., 2006) to include software assisted data-prefetching and an improved memory model. Software-based prefetching is becoming an increasingly important feature in modern processors but to the best of our knowledge, existing frameworks do not model it. Our model uses micro-architecture independent application characteristics to predict CPI with an average error of less than 10% when validated against the Itanium-2 processor. Besides accurate performance prediction, we illustrate the applications of the model to processor bottleneck analysis, workload characterization and design space exploration

We describe computational methods to solve the problem of identifying novel proteins from tandem mass spectrometry (tandem MS or MS/MS) data and introduce new approaches that will give more accurate solutions. These new approaches integrate chemical information and knowledge into a graph-theoretic framework. Two sources of chemical information that we investigate are mass tagging and dissociation chemistry in the tandem MS process itself. We describe machine learning techniques that are used to classify peaks according to ion types based on known dissociation chemistry. We describe the algorithms that are implemented in a software code called PepSUMS. Using PepSUMS, we give results on the effectiveness of the new methods on the ultimate goal of improved protein identification.

We propose and validate a closed-end, analytical, general, predictive performance model for applications based on wavefront algorithms on clusters of SMPs. Wavefront algorithms are ubiquitous in parallel computing, since they represent a means of enabling parallelism in computations that contain recurrences. Our particular interest in wavefront algorithms derives from their use in discrete ordinates neutral particle transport computations representative of ASCI, but other important uses are well known. The proposed model captures the tradeoff between processor utilization and communication requirements characteristics of wavefront algorithms. The general model can predict the performance of this class of applications on distributed architectures with a network of lower dimensionality compared to that of an MPP, of which clusters of SMPs are one example. We validate the model using a compact-application from the ASCI workload on a large-scale cluster of SGI Origin 2000s in existence at the Los Alamos National Laboratory. The proposed model validates well on all clusters configurations utilized.

In this paper the authors compare single processor performance of the SGI Origin and PowerChallenge and utilize a previously reported performance model for hierarchical memory systems to explain the results. Both the Origin and PowerChallenge use the same microprocessor (MIPS R10000) but have significant differences in their memory subsystems. Their memory model includes the effect of overlap between CPU and memory operations and allows them to infer the individual contributions of all three improvements in the Origin`s memory architecture and relate the effectiveness of each improvement to application characteristics.

Abstract In this paper we present functional Id and Haskell versions of a large Monte Carlo radiation transport code, and compare the two languages with respect to their expressiveness. Monte Carlo transport simulation exercises such abilities as parsing, input/output, recursive data structures and traditional number crunching, which makes it a good test problem for languages and compilers. Using some code examples, we compare the programming styles encouraged by the two languages. In particular, we discuss the effect of laziness on programming style. We point out that resource management problems currently prevent running realistically large problem sizes in the functional versions of the code.

The authors present the results of an architectural comparison of SIMD (single-instruction multiple-data) massive parallelism, as implemented in the Thinking Machines Corp. CM-2, and vector or concurrent-vector processing, as implemented in the Cray Research Inc., Y-MP/8. The comparison is based primarily upon three application codes taken from the LANL (Los Alamos National Laboratory) CM-2 workload. Tests were run by porting CM Fortran codes to the Y-MP, so that nearly the same level of optimization was obtained on both machines. The results for fully configured systems, using measured data rather than scaled data from smaller configurations, show that the Y-MP/8 is faster than the 64 k CM-2 for all three codes. A simple model that accounts for the relative characteristic computational speeds of the two machines, and reduction in overall CM-2 performance due to communication or SIMD conditional execution, accurately predicts the performance of two of the three codes. The authors show the similarity of the CM-2 and Y-MP programming models and comment on selected future massively parallel processor designs.>

The performance of two second-generation supercomputers, the NEC SX-3 and the Fujitsu VP2600, is analyzed using the Standard Los Alamos Benchmark Set, the Mendez Fluid Dynamics Codes, and some highly vectorizable production-type codes from Los Alamos.For comparison, data are also given for a single processor of the CRAY Y-MP8/264.Factors affecting performance such as memory bandwidth, vector register organization, and the effects of multiple vector pipelines are examined.On a highly vectorizable code that can take advantage of multiple vector pipes, the SX-3 and VP2600 are faster than the single CRAY Y-MP processor by factors of seven to eight.

Fully vectorized versions of the Los Alamos National Laboratory benchmark code Gamteb, a Monte Carlo photon transport algorithm, were developed for the Cyber 205/ETA-10 and Cray X-MP/Y-MP architectures. Single-processor performance measurements of the vector and scalar implementations were modeled in a modified Amdahl's Law that accounts for additional data motion in the vector code. The performance and implementation strategy of the vector codes are related to architectural features of each machine. Speedups between fifteen and eighteen for Cyber 205/ETA-10 architectures, and about nine for CRAY X-MP/Y-MP architectures are observed. The best single processor execution time for the problem was 0.33 seconds on the ETA-10G, and 0.42 seconds on the CRAY Y-MP.

The authors present a performance evaluation and comparison of the Hitachi S-820/80 supercomputer on a set of standard Fortran benchmark codes that range from simple kernels to fluid dynamics applications. They find that the S-820 is a great deal faster in vector mode than any other supercomputer they have measured, with almost twice the performance on highly vectorized codes than the fastest machine they have seen up to now, the NEC SX-2. In scalar mode, however, the S-820 is roughly even with the X-MP and the SX-2, with a slight advantage going to the SX-2 in the applications tested.>