Predicated Partial Redundancy Elimination using a Cost Analysis

Partial Redundancy Elimination (PRE) is a general scheme for suppressing partial redundancies which encompasses traditional optimizations like loop invariant code motion and redundant code elimination. In this paper we address the problem of performing this optimization interprocedurally. We use interprocedural partial redundancy elimination for placement of communication and communication preprocessing statements while compiling for distributed memory parallel machines.

Partial redundancy elimination (PRE) is an interesting compiler optimization because of its effectiveness and generality. Among many PRE algorithms, the one in static single assignment form (SSAPRE) has benefits over other bit-vector-based PRE algorithms. It preserves the properties of the SSA form after PRE and exploits the sparsity of the SSA form, resulting in reduced analysis and optimization time. This paper presents a practical improvement of the SSAPRE algorithm that further reduces the analysis and optimization time. The underlying idea is removing unnecessary Φ’s during the Φ-Insertion phase that is the first step of SSAPRE. We classify the expressions into three categories: confined expressions, local expressions, and the others. We show that unnecessary Φ’s for confined and local expressions can be easily detected and removed. We implement our locality-based SSAPRE algorithm in a C compiler and evaluate its effectiveness with 20 applications from SPEC benchmark suites. In our measurements, on average 91% of Φ’s identified by the original demand-driven SSAPRE algorithm are unnecessary for PRE. Pruning these unnecessary Φ’s in the Φ-Insertion phase makes our locality-based SSAPRE algorithm 1.8 times faster, on average, than the original SSAPRE algorithm.

We develop an expansion-based algorithm for semantic partial redundancy elimination (SPRE), which overcomes the central drawbacks of the state-of-the-art approaches, which leave the program structure invariant: they fail to eliminate all partial redundancies even for acyclic programs. Besides being optimal for acyclic programs, our algorithm is unique in eliminating all partial k-redundancies, a new class of redundancies which is characterised by the number k of loop iterations across which values have to be kept. These optimality results come at the price of an in the worst case exponential program growth. The new technique is thus tailored for optimizing the typically considerably small computational “hot” spots of a program. Here it is particularly promising because its structural simplicity supports extensions to uniformly capture further powerful optimisations like constant propagation or strength reduction in a mutually supportive way.

Canonical Wnt (cWnt) signalling is involved in a plethora of basic developmental processes such as endomesoderm specification, gastrulation and patterning the main body axis. To activate the signal, Wnt ligands form complexes with LRP5/6 and Frizzled receptors, which leads to nuclear translocation of β-catenin and a transcriptional response. In Bilateria, the expression of different Frizzled genes is often partially overlapping, and their functions are known to be redundant in several developmental contexts. Here, we demonstrate that all four Frizzled receptors take part in the cWnt-mediated oral-aboral axis patterning in the cnidarian Nematostella vectensis but show partially redundant functions. However, we do not see evidence for their involvement in the specification of the endoderm - an earlier event likely relying on maternal intracellular β-catenin signalling components. Finally, we demonstrate that the main Wnt ligands crucial for the early oral-aboral patterning are Wnt1, Wnt3 and Wnt4. Comparison of our data with knowledge from other models suggests that distinct but overlapping expression domains and partial functional redundancy of cnidarian and bilaterian Frizzled genes may represent a shared ancestral trait.

The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning (“PDR”). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model (“GMM”), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model (“GHM”). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.

Research in bidirectional data flow analysis seems to have come to a halt due to an impression that the case for bidirectional data flow analysis has been considerably weakened by a plethora of investigations based on decomposability of known bidirectional placement algorithms into a sequence of purely unidirectional components. This paper shows that the approach of decomposability is not general enough in that it derives its power from the simplifying graph transformation of edge-splitting and the favourable nature of flows in partial redundancy elimination (PRE). This follows from the fact that in the absence of edge-splitting, PRE cannot be performed using a sequence of cascaded unidirectional flows. Further, edge-splitting inherently converts data flows involved in PRE into unidirectional flows. In our opinion, this obviates the need of an alternative formulation. We also show that edge-splitting cannot convert data flows involved in "truly" bidirectional data flow problems into unidirectional flows. Thus not every bidirectional data flow problem can be converted into unidirectional flows. Besides, we argue that the premise that bidirectional analysis is more complex than unidirectional analysis, is invalid. We also mention some issues in bidirectional data flow analysis which need to be investigated.

Cobalt: A Language for Writing Provably-Sound Compiler Optimizations

Partial redundancy elimination (PRE) is one of the most important and widespread optimizations in compilers. However, current PRE-techniques are inadequate to handle predicated code, i.e., programs where instructions are guarded by a 1-bit register that dynamically controls whether the effect of an instruction should be committed or nullified. In fact, to exclude corrupting the semantics they must be overly conservative making them close to useless. Since predicated code will be more and more common with the advent of the IA-64 architecture, we present here a family of PRE-algorithms tailored for predicated code. Conceptually, the core element of this family can be considered the counterpart of busy code motion of [17]. It can easily be tuned by two orthogonal means. First, by adjusting the power of a preprocess feeding it by information on predication. Second, by relaxing or strengthening the constraints on synthesizing predicates controlling the movability of computations. Together with extensions towards lazy code motion, this results in a family of PRE-algorithms spanning a range from tamed to quite aggressive algorithms, which is illustrated by various meaningful examples.

Instruction schedulers employ code motion as a means of instruction reordering to enable scheduling of instructions at points where the resources required for their execution are available. In addition, driven by the profiling data, schedulers take advantage of predication and speculation for aggressive code motion across conditional branches. Optimization algorithms for partial dead code elimination (PDE) and partial redundancy elimination (PRE) employ code sinking and hoisting to enable optimization. However, unlike instruction scheduling, these optimization algorithms are unaware of resource availability and are incapable of exploiting profiling information, speculation, and predication. In this paper we develop data flow algorithms for performing the above optimizations with the following characteristics: (i) opportunities for PRE and PDE enabled by hoisting and sinking are exploited; (ii) hoisting and sinking of a code statement is driven by availability of functional unit resources; (iii) predication and speculation is incorporated to allow aggressive hoisting and sinking; and (iv) path profile information guides predication and speculation to enable optimization.

Contrary to common belief, one can achieve better reliability by triplicating only part of a computing machine rather than by triplicating the whole machine. The reliability figures for varying degrees of partial redundancy have been obtained. From such figures are derived conditions best suited to the totally redundant, partially redundant or nonredundant system.

How might organisms grow into their desired physical forms in spite of environmental and genetic variation? How do they maintain this form in spite of physical insults? This article presents a case study in simulated morphogenesis, using a physics-based model for embryonic epithelial tissue. The challenges of the underlying physics force the introduction of closed-loop controllers for both spatial patterning and geometric structure. Reliable development is achieved not through elaborate control procedures or exact solutions, but through crude layering of independent, overlapping mechanisms. As a consequence, development and regeneration together become one process, morphological homeostasis, which, owing to its internal feedbacks and partially redundant architecture, is remarkably robust to both knockout damage and environmental variation. The incomplete nature of such redundancy furnishes an evolutionary rationale for its preservation, in spite of individual knockout experiments that may suggest it has little purpose.

Persistent latent infection of Epstein-Barr Virus (EBV) is associated with various human malignancies such as Burkitt’s lymphoma, gastric (GC) and nasopharyngeal carcinoma (NPC). Several studies have reported the abundance of EBV-encoded alternatively spliced BamHI A rightward transcripts (BARTs) in these malignancies, of which NPC confers the highest tissue expression. However, extensive investigations of their potential use as tissue- and/or blood-based NPC biomarkers, particularly for Hong Kong cases, remain elusive.
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\nComparison of microRNA (miRNA) profiles between tumor and non-tumor specimens from 5 paired NPC cases reveal 29/39 of EBV miRNAs were significantly upregulated in tumor tissues versus their non-tumor counterparts (P<0.05). Microarray-based results also indicate that EBV miRNAs, particularly from the BART clusters, are generally more upregulated than miRNAs of human origin. Of the upregulated EBV miRNAs, 12 were chosen for further validation in 15 additional paired NPC cases by qRT-PCR and yielded consistent results with the array. BART6-5p, 6-3p, 14, 18-5p, 19-3p and 2-5p were also distinctly detected in the serum of NPC patients, and positively correlated with corresponding tumor copy numbers.
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\nGiven that successfully validated EBV BARTs were also present at functional levels in tumors. Their functional role(s) were further investigated to identify potential oncogenic ‘driver’ BART miRNA(s). Like miRNAs of human origin, BARTs can also downregulate ‘target’ genes conferring 3’UTR sites with partial homology to their miRNA seed region. Bioinformatic tools were used to generate putative target genes of upregulated BARTs, whereby functional clustering reveals Wnt signaling as the most enriched pathway. Experimental target validation shows partial redundancy between several Wnt inhibitors (APC, GSK3B, BTRC, NLK) by a subset of BARTs (6-3p, 14, 17-5p, 19-3p). Gain- and loss-of-function studies pinpoint BART19-3p as a potential Wnt ‘driver’. Given its ability to enhance .-catenin stability and attenuate ubiquitin-mediated proteolysis to promote intranuclear s-catenin accumulation, BART19-3p-mediated TCF/LEF activation promoted tumor growth (via CCND1), self-renewal (via CMYC, CD44), EMT (via SNAIL) and invasion (via MMP7) in EBV negative cell lines. BART19-3p silencing in C666 sufficiently rescued Wnt regulatory systems and modulated Wnt/Hedgehog crosstalk via GSK3B/BTRC-mediated GLI3 processing.
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\nIn addition to BART miRNA profiling, the mechanism-based approach of this study provides novel and interesting insights into EBV-mediated oncogenesis. Partially redundant and likely additive effects of BARTs may be important in finetuning the dose-dependent effects of aberrant signal transduction pathways in NPC. A better understanding of BART-mediated regulation of Wnt signaling and their ability to recapitulate frequently observed nuclear accumulation of t-catenin in primary NPC specimens also helps pinpoint potential druggable targets (e.g. BART19-3p). Considering the difficulties in targeting mutation-independent aberrant Wnt signaling in cancer and the advent of miRNA-based targeted therapy, ‘driver’ BART(s) may be promising therapeutic targets for improving the currently poor treatment outcomes of advanced stage NPC. Alternatively, the specificity of BARTs within NPC tumors and patient sera could also be harnessed to develop less invasive means for early detection, which like majority of cancers yields better prognosis.

When analyzing programs for value recomputation, one faces the problem of naming the value that flows between equivalent computations with different lexical names. This paper presents a data-flow analysis framework that overcomes this problem by synthesizing a name space tailored for tracing the values whose flow is of interest to a given data-flow problem. Furthermore, to exploit recomputation of a value with multiple, synonymous names, path-sensitive value numbering on the synthetic name space is developed. Optimizations that rely on value flow to detect redundant computations, such as partial redundancy elimination and constant propagation, become more powerful when phrased in our framework.The framework is built on a new program representation called Value Name Graph (VNG) which gains its power from integrating three orthogonal techniques: symbolic back-substitution, value numbering, and data-flow analysis. Our experiments with the implementation show that analysis on the VNG is practical in both time and space.

The traditional tradeoff when performing dynamic compilation is that of fast compilation time versus fast code performance. Most dynamic compilation systems for Java perform selective compilation and/or optimization at a method granularity. This is the not the optimal granularity level. However, compiling at a sub-method granularity is thought to be too complicated to be practical. This paper describes a straightforward technique for performing compilation and optimizations at a finer, sub-method granularity. We utilize dynamic profile data to determine intra-method code regions that are rarely or never executed, and compile and optimize the code without those regions. If a branch that was predicted to be rare is actually taken at run time, we fall back to the interpreter or dynamically compile another version of the code. By avoiding compiling and optimizing code that is rarely executed, we are able to decrease compile time significantly, with little to no degradation in performance. Futhermore, ignoring rarely-executed code can open up more optimization opportunities on the common paths. We present two optimizations---partial dead code elimination and rare-path-sensitive pointer and escape analysis---that take advantage of rare path information. Using these optimizations, our technique is able to improve performance beyond the compile time improvements

Nowadays, manufacturing companies are characterized by complex systems with multiple products being manufactured in multiple assembly lines. In such situations, traditional costing systems based on deterministic cost models cannot be used. This paper focuses on developing a stochastic approach to costing systems that considers the variability in the process cycle time of the different workstations in the assembly line. This approach provides a range of values for the product costs, allowing for a better perception of the risk associated to these costs instead of providing a single value of the cost. The confidence interval for the mean and the use of quartiles one and three as lower and upper estimates are proposed to include variability and risk in costing systems. The analysis of outliers and some statistical tests are included in the proposed approach, which was applied in a tier 1 company in the automotive industry. The probability distribution of the possible range of values for the bottleneck’s cycle time showcase all the possible values of product cost considering the process variability and uncertainty. A stochastic cost model allows a better analysis of the margins and optimization opportunities as well as investment appraisal and quotation activities.