Browsing by Author "Lilja, David J."

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    Dynamic Code Region-based Program Phase Classification and Transition Prediction
    (2005-05-23) Kim, Jinpyo; Kodakara, Sreekumar V.; Hsu, Wei-Chung; Lilja, David J.; Yew, Pen-Chung
    Detecting and predicting a program's execution phases is crucial to dynamically adaptable systems and dynamic optimizations. Program execution phases have a strong connection to program control structures, in particular, loops and procedure calls. Intuitively, a phase can be associated with some dynamic code regions that are embedded in loops and procedures. This paper proposes off-line and on-line analysis techniques could effectively identify and predict program phases by exploiting program control flow information. For off-line analyses, we introduce a dynamic interval analysis method that converts the complete program execution into an annotated tree with statistical information attached to each dynamic code region. It can efficiently identify dynamic code regions associated with program execution phases at different granularities. For on-line analyses, we propose new phase tracking hardware which can effectively classify program phases and predict next execution phases. We have applied our dynamic interval analysis method on 10 SPEC CPU2000 benchmarks. We demonstrate that the change in program behavior has strong correlation with control transfer between dynamic code regions. We found that a small number of dynamic code regions can represent the whole program execution with high code coverage. Our proposed on-line phase tracking hardware feature can effectively identify a stable phase at a given granularity and very accurately predict the next execution phase.
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    NORTHSTAR: A Parameter Estimation Method for the Spatial Autoregression Model
    (2007-02-09) Celik, Mete; Kazar, Baris M.; Shekhar, Shashi; Boley, Daniel; Lilja, David J.
    Parameter estimation method for the spatial autoregression model (SAR) is important because of the many application domains, such as regional economics, ecology, environmental management, public safety, transportation, public health, business, travel and tourism. However, it is computationally very expensive because of the need to compute the determinant of a large matrix due to Maximum Likelihood Theory. The limitation of previous studies is the need for numerous computations of the computationally expensive determinant term of the likelihood function. In this paper, we present a faster, scalable and NOvel pRediction and estimation TecHnique for the exact SpaTial Auto Regression model solution (NORTHSTAR). We provide a proof of the correctness of this algorithm by showing the objective function to be unimodular. Analytical and experimental results show that the NORTHSTAR algorithm is computationally faster than the related approaches, because it reduces the number of evaluations of the determinant term in the likelihood function.
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    PASS: Program Structure Aware Stratified Sampling for Statistically Selecting Instruction Traces and Simulation Points
    (2005-12-30) Kodakara, Sreekumar V.; Kim, Jinpyo; Hsu, Wei-Chung; Lilja, David J.; Yew, Pen-Chung
    As modeled microarchitectures become more complex and the size of benchmark program keeps increasing, simulating a complete program with various input sets is practically infeasible within a given time and computation resource budget. A common approach is to simulate only a subset of representative parts of the program selected from the complete program execution. SimPoint [1,2] and SMARTS [10] have shown that accurate performance estimates can be achieved with a relatively small number of instructions. This paper proposes a novel method called Program structure Aware Stratified Sampling (PASS) for further reducing microarchitecture simulation time without losing accuracy and coverage. PASS has four major phases, consisting of building Extended Calling Context Tree (ECCT), dynamic code region analysis, program behavior profiling, and stratified sampling. ECCT is constructed to represent program calling context and repetitive behavior via dynamic instrumentation. Dynamic code region analysis identifies code regions with similar program phase behaviors. Program behavior profiling stores statistical information of program behaviors such as number of executed instructions, branch mispredictions, and cache miss associated with each code region. Based on the variability of each phase, we adaptively sample instances of instruction streams through stratified sampling. We applied PASS on 12 SPEC CPU2000 benchmark and input combinations and achieved average 1.46 % IPC error bound from measurements of native execution on Itanium-2 machine with much smaller sampled instruction streams.