Lecture Notes in Computer Science 4917

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Phase Complexity Surfaces: Characterizing Time-Varying Program Behavior 321 programs may exhibit phase behavior at various time scales, and the phase behavior may be hierarchical, i.e., a phase at one time scale may consist of multiple phases at a finer time scale. – The second property is the number of phases a program goes through at run-time. Some programs repeatedly stay in the same phase, for example when executing the same piece of code over and over again; other programs may go through many distinct phases. – The third property concerns the variability within phases versus the variability across phases. The premise of phase behavior is that there is less variability within a phase than across phases, i.e., the variability in behavior for intervals belonging to a given phase is fairly small compared to intervals belonging to different phases. – The fourth and final property relates to the predictability of the program phase behavior. For some programs, its time-varying behavior is very regular and by consequence very predictable. For other programs on the other hand, time-varying behavior is rather complex, irregular and hard to predict. Obviously, all four properties are related to each other. More in particular, the time scale determines the number of phases to be found with a given degree of homogeneity within each phase; the phases found, in their turn, affect the predictability of the phase behavior. By consequence, getting a good understanding of a program’s phase behavior requires all four properties be characterized simultaneously. This paper presents phase complexity surfaces as a way to characterize program phase behavior. The important benefit over prior work in characterizing program phase behavior is that phase complexity surfaces capture all of the four properties mentioned above in a unified and intuitive way while enabling the reasoning in terms of these four properties individually. As a subsequent step, we use these phase complexity surfaces to characterize and classify programs in terms of their phase behavior. Within SPEC CPU2000 we identify a number of prominent groups of programs with similar phase behavior. Researchers can use this classification to select benchmarks for their studies in exploiting program phase behavior. 2 Related Work There exists a large body of related work on program phase behavior. In this section, we only discuss the issues covered in prior work that relate most closely to this paper. Granularity. The granularity at which time-varying behavior is studied and exploited varies widely. Some researchers look for program phase behavior at the 100K instruction interval size [1,6,7]; others look for program phase behavior at the 1M or 10M instruction interval granilarity [23]; and yet others identify phase behavior at yet a larger granularity of 100M or even 1B instructions [22,26]. The granularity chosen obviously depends on the purpose of the phase-level optimization. The advantage of a small time scale is that the optimization can potentially achieve better performance because the optimization can be applied more aggressively. A larger time scale on the other hand has the advantage that the overhead of exploiting the phase behavior can be amortized more easily.
322 F. Vandeputte and L. Eeckhout Some researchers study phase behavior at different time scales simultaneously. Wavelets for example provide a natural way of characterizing phase behavior at various time scales [4,13,24], and Lau et al. [17] identify a hierarchy of phase behavior. Fixed-length versus variable-length phases. Various researchers aim at detecting phase behavior by looking into fixed-length instruction intervals [1,6,7]. The potential problem with the fixed-length interval approach though is that in some cases it may be hard to identify phase behavior because of the effect of dissonance between the fixedlength interval and the natural period of the phase behavior. In case the length of the fixed-length interval is slightly smaller or bigger than the period of the phase behavior, the observation made will be out of sync with the natural phase behavior. To address this issue, some researchers advocate identifying phases using variable-length intervals. Lau et al. [17] use pattern matching to find variable-length intervals, and in their follow-on work [16] they identify program phases by looking into a program’s control flow structure consisting of loops, and methods calls and returns. Huang et al. [12] detect (variable-length) phases at method entry and exit points by tracking method calls via a call stack. Microarchitecture-dependent versus microarchitecture-independent characterization. Identifying phases can be done in a number of ways. Some identify program phase behavior by inspecting microarchitecture-dependent program behavior, i.e., they infer phase behavior from inspecting time-varying microarchitecture performance numbers. For example, Balasubramonian et al. [1] collect CPI and cache miss rates. Duesterwald et al. [8] collect IPC numbers, cache miss rates and branch misprediction rates. Isci and Martonosi [14] infer phase behavior from power vectors. A concern with microarchitecture-dependent based phase detection is that once phase behavior is being exploited, it may affect the microarchitecture-dependent metrics being measured; this potentially leads to the problem where it is unclear whether the observed time-varying behavior is a result of natural program behavior or is a consequence of exploiting the observed phase behavior. An alternative approach is to measure microarchitecture-independent metrics to infer phase behavior from. Dhodapkar and Smith [7,6] for example keep track of a program’s working set; when the working set changes, they infer that the program transitions to another phase. Sherwood et al. [26] use Basic Block Vectors (BBVs) to keep track of the basic blocks executed — BBVs are shown to correlate well with performance in [18]. Other microarchitecture-independent metrics are for example memory addresses [13] and data reuse distances [24], a program’s control flow structure such as loops and methods [11,12,16], a collection of program characteristics such as instruction mix, ILP, memory access patterns, etc. [9,19]. Phase classification. Different researchers have come up with different approaches to partitioning instruction intervals into phases. Some use threshold clustering [6,7,27]; others use machine learning techniques such as k-means clustering [26], pattern matching [17,24]; yet others use frequency analysis through wavelets [4,5,13,24]. Phase prediction. An important aspect to exploiting phase behavior is to be able to predict and anticipate future phase behavior. Sherwood et al. [27] proposed last phase, RLE and Markov phase predictors. In their follow-on work [20], they added confidence
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Lecture Notes in Computer Science 4
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Volume Editors Per Stenström Chalm
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VI Preface The planning of a confer
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VIII Organization Mahmut Kandemir P
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Invited Program Table of Contents S
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Table of Contents XIII Turbo-ROB: A
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4 M. Valero and J. Labarta future s
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MIPS MT: A Multithreaded RISC Archi
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2.2 VPEs as Scheduling Domains MIPS
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MIPS MT: A Multithreaded RISC Archi
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6.1 Thread Context Virtualization M
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8 Experimental Results MIPS MT: A M
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Cycles/ Iteration MIPS MT: A Multit
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9 Conclusions MIPS MT: A Multithrea
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MPI: Message Passing on Multicore P
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overhead per word (cycles) 1200 100
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speedup 3.5 3 2.5 2 1.5 1 0.5 0 rMP
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speedup 9 8 7 6 5 4 3 2 1 0 rMPI: M
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Modeling Multigrain Parallelism on
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BRAM-LUT Tradeoff on a Polymorphic
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CLI as an Effective Deployment Form
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Compilation Strategies for Reducing
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Experiences with Parallelizing a Bi
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RTL for each Component * Gate−lev
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Variation-Aware Software Techniques
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244 Y. Sazeides et al. of mispredic
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246 Y. Sazeides et al. Affector BB1
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248 Y. Sazeides et al. 2.3 How to U
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250 Y. Sazeides et al. Cumulative D
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254 Y. Sazeides et al. Misses/KI 12
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Turbo-ROB: A Low Cost Checkpoint/Re
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260 P. Akl and A. Moshovos Original
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264 P. Akl and A. Moshovos new firs
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400 Author Index Shajrawi, Yousef 3
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