Control-flow speculation through value prediction for superscalar processors

In this paper, we introduce a new branch predictor that predicts the outcomes of branches by predicting the value of their inputs and performing an early computation of their results according to the predicted values. The design of a hybrid predictor comprising our branch predictor and a correlating branch predictor is presented. We also propose a new selector that chooses the most reliable prediction for each branch. This selector is based on the path followed to reach the branch. Results for immediate updates show a significant improvement with respect to a conventional hybrid predictor for different size configurations. In addition, the proposed hybrid predictor with a size of 8 KB achieves the same miss ratio as a conventional one of 64 KB. Performance evaluation for a dynamically-scheduled superscalar processor, with realistic updates, shows a speed-up of 11% despite its higher latency (up to 4 cycles)Peer ReviewedPostprint (published version

Depressive symptoms in asymptomatic stage B heart failure with Type II diabetic mellitus.

BackgroundThe presence of concomitant Type II diabetic mellitus (T2DM) and depressive symptoms adversely affects individuals with symptomatic heart failure (HF).HypothesisIn presymptomatic stage B HF, this study hypothesized the presence of greater inflammation and depressive symptoms in T2DM as compared to non-T2DM Stage B patients.MethodsThis cross-sectional study examined clinical parameters, inflammatory biomarkers, and depressive symptoms in 349 T2DM and non-T2DM men with asymptomatic stage B HF (mean age 66.4 years ±10.1; range 30-91).ResultsFewer diabetic HF patients had left ventricular (LV) systolic dysfunction (P < .05) although more had LV diastolic dysfunction (P < .001). A higher percentage of T2DM HF patients were taking ACE-inhibitors, beta-blockers, calcium channel blockers, statins, and diuretics (P values < .05). T2DM HF patients had higher circulating levels of interleukin-6 (IL-6) (P < .01), tumor necrosis factor-alpha (P < .01), and soluble ST2 (sST2) (P < .01) and reported more somatic/affective depressive symptoms (Beck Depression Inventory II) (P < .05) but not cognitive/affective depressive symptoms (P = .20). Among all patients, in a multiple regression analysis predicting presence of somatic/affective depressive symptoms, sST2 (P = .026), IL-6 (P = .010), B-type natriuretic peptide (P = .016), and sleep (Pittsburgh Sleep Quality Index [P < .001]) were significant predictors (overall model F = 15.39, P < .001, adjusted R2 = .207).ConclusionsSomatic/affective but not cognitive/affective depressive symptoms are elevated in asymptomatic HF patients with T2DM patients. Linkages with elevated inflammatory and cardiac relevant biomarkers suggest shared pathophysiological mechanisms among T2DM HF patients with somatic depression, and these conditions are responsive to routine interventions, including behavioral. Copyright © 2019 John Wiley & Sons, Ltd

The weakening of branch predictor performance as an inevitable side effect of exploiting control independence

Many algorithms are inherently sequential and hard to explicitly parallelize. Cores designed to aggressively handle these problems exhibit deeper pipelines and wider fetch widths to exploit instruction-level parallelism via out-of-order execution. As these parameters increase, so does the amount of instructions fetched along an incorrect path when a branch is mispredicted. Many of the instructions squashed after a branch are control independent, meaning they will be fetched regardless of whether the candidate branch is taken or not. There has been much research in retaining these control independent instructions on misprediction of the candidate branch. This research shows that there is potential for exploiting control independence since under favorable circumstances many benchmarks can exhibit 30% or more speedup. Though these control independent processors are meant to lessen the damage of misprediction, an inherent side-effect of fetching out of order, branch weakening, keeps realized speedup from reaching its potential. This thesis introduces, formally defines, and identifies the types of branch weakening. Useful information is provided to develop techniques that may reduce weakening. A classification is provided that measures each type of weakening to help better determine potential speedup of control independence processors. Experimentation shows that certain applications suffer greatly from weakening. Total branch mispredictions increase by 30% in several cases. Analysis has revealed two broad causes of weakening: changes in branch predictor update times and changes in the outcome history used by branch predictors. Each of these broad causes are classified into more specific causes, one of which is due to the loss of nearby correlation data and cannot be avoided. The classification technique presented in this study measures that 45% of the weakening in the selected SPEC CPU 2000 benchmarks are of this type while 40% involve other changes in outcome history. The remaining 15% is caused by changes in predictor update times. In applying fundamental techniques that reduce weakening, the Control Independence Aware Branch Predictor is developed. This predictor reduces weakening for the majority of chosen benchmarks. In doing so, a control independence processor, snipper, to attain significantly higher speedup for 10 out of 15 studied benchmarks

Prediction of the permeability of neutral drugs inferred from their solvation properties

Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay

FPGA configuration of an alloyed correlated branch predictor used with RISC processor for educational purposes

Instructions pipelining is one of the most outstanding techniques used in improving processor speed; nonetheless, these pipelined stages are constantly facing stalls that caused by nested conditional branches. During the execution of nested conditional branches, the behavior of the running branch depends on the history information of the previous ones; therefore, these branches have the greatest effect in reducing the prediction accuracy of a branch predictor among conditional branches. The purpose of this research is to reduce the stall cycles caused by correlated branches misprediction by introducing a hardware model of a branch predictor that combines both local and global prediction techniques. This predictor integrates the prediction characteristics of the alloyed predictor with those of the correlated predictor. the predictor design which implemented in VHDL (Very high-speed IC hardware description language) was inserted in previously designed MIPS (microprocessor without interlocked pipelined stages) processor and its prediction accuracy was confirmed by executing a program using the selection sort algorithm to sort 100 input numbers of different combinations ascendingly

All mixed up? Finding the optimal feature set for general readability prediction and its application to English and Dutch

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, though NLP-inspired research has focused on adding more complex readability features there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and a crowd, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring a deep linguistic processing, resulting in ten different feature groups. Both a regression and classification setup are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task which provides considerable insights in which feature combinations contribute to the overall readability prediction. Since we also have gold standard information available for those features requiring deep processing we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully-automatic readability prediction pipeline is on par with the pipeline using golden deep syntactic and semantic information

Lost in translation: Exposing hidden compiler optimization opportunities

Existing iterative compilation and machine-learning-based optimization techniques have been proven very successful in achieving better optimizations than the standard optimization levels of a compiler. However, they were not engineered to support the tuning of a compiler's optimizer as part of the compiler's daily development cycle. In this paper, we first establish the required properties which a technique must exhibit to enable such tuning. We then introduce an enhancement to the classic nightly routine testing of compilers which exhibits all the required properties, and thus, is capable of driving the improvement and tuning of the compiler's common optimizer. This is achieved by leveraging resource usage and compilation information collected while systematically exploiting prefixes of the transformations applied at standard optimization levels. Experimental evaluation using the LLVM v6.0.1 compiler demonstrated that the new approach was able to reveal hidden cross-architecture and architecture-dependent potential optimizations on two popular processors: the Intel i5-6300U and the Arm Cortex-A53-based Broadcom BCM2837 used in the Raspberry Pi 3B+. As a case study, we demonstrate how the insights from our approach enabled us to identify and remove a significant shortcoming of the CFG simplification pass of the LLVM v6.0.1 compiler.Comment: 31 pages, 7 figures, 2 table. arXiv admin note: text overlap with arXiv:1802.0984