The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
Blaine A. Pfeifer - One of the best experts on this subject based on the ideXlab platform.
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improved heterologous production of the nonribosomal peptide polyketide siderophore yersiniabactin through metabolic engineering and Induction optimization
Biotechnology Progress, 2016Co-Authors: Mahmoud Kamal Ahmadi, Blaine A. PfeiferAbstract:Biosynthesis of complex natural products like polyketides and nonribosomal peptides using Escherichia coli as a heterologous host provides an opportunity to access these molecules. The value in doing so stems from the fact that many compounds hold some therapeutic or other beneficial property and their original production hosts are intractable for a variety of reasons. In this work, metabolic engineering and Induction Variable optimization were used to increase production of the polyketide-nonribosomal peptide compound yersiniabactin, a siderophore that has been utilized to selectively remove metals from various solid and aqueous samples. Specifically, several precursor substrate support pathways were altered through gene expression and exogenous supplementation in order to boost production of the final compound. The gene expression Induction process was also analyzed to identify the temperatures and inducer concentrations resulting in highest final production levels. When combined, yersiniabactin production was extended to ∼175 mg L-1 . © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1412-1417, 2016.
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Improved heterologous production of the nonribosomal peptide‐polyketide siderophore yersiniabactin through metabolic engineering and Induction optimization
Biotechnology progress, 2016Co-Authors: Mahmoud Kamal Ahmadi, Blaine A. PfeiferAbstract:Biosynthesis of complex natural products like polyketides and nonribosomal peptides using Escherichia coli as a heterologous host provides an opportunity to access these molecules. The value in doing so stems from the fact that many compounds hold some therapeutic or other beneficial property and their original production hosts are intractable for a variety of reasons. In this work, metabolic engineering and Induction Variable optimization were used to increase production of the polyketide-nonribosomal peptide compound yersiniabactin, a siderophore that has been utilized to selectively remove metals from various solid and aqueous samples. Specifically, several precursor substrate support pathways were altered through gene expression and exogenous supplementation in order to boost production of the final compound. The gene expression Induction process was also analyzed to identify the temperatures and inducer concentrations resulting in highest final production levels. When combined, yersiniabactin production was extended to ∼175 mg L-1 . © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1412-1417, 2016.
Mahmoud Kamal Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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improved heterologous production of the nonribosomal peptide polyketide siderophore yersiniabactin through metabolic engineering and Induction optimization
Biotechnology Progress, 2016Co-Authors: Mahmoud Kamal Ahmadi, Blaine A. PfeiferAbstract:Biosynthesis of complex natural products like polyketides and nonribosomal peptides using Escherichia coli as a heterologous host provides an opportunity to access these molecules. The value in doing so stems from the fact that many compounds hold some therapeutic or other beneficial property and their original production hosts are intractable for a variety of reasons. In this work, metabolic engineering and Induction Variable optimization were used to increase production of the polyketide-nonribosomal peptide compound yersiniabactin, a siderophore that has been utilized to selectively remove metals from various solid and aqueous samples. Specifically, several precursor substrate support pathways were altered through gene expression and exogenous supplementation in order to boost production of the final compound. The gene expression Induction process was also analyzed to identify the temperatures and inducer concentrations resulting in highest final production levels. When combined, yersiniabactin production was extended to ∼175 mg L-1 . © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1412-1417, 2016.
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Improved heterologous production of the nonribosomal peptide‐polyketide siderophore yersiniabactin through metabolic engineering and Induction optimization
Biotechnology progress, 2016Co-Authors: Mahmoud Kamal Ahmadi, Blaine A. PfeiferAbstract:Biosynthesis of complex natural products like polyketides and nonribosomal peptides using Escherichia coli as a heterologous host provides an opportunity to access these molecules. The value in doing so stems from the fact that many compounds hold some therapeutic or other beneficial property and their original production hosts are intractable for a variety of reasons. In this work, metabolic engineering and Induction Variable optimization were used to increase production of the polyketide-nonribosomal peptide compound yersiniabactin, a siderophore that has been utilized to selectively remove metals from various solid and aqueous samples. Specifically, several precursor substrate support pathways were altered through gene expression and exogenous supplementation in order to boost production of the final compound. The gene expression Induction process was also analyzed to identify the temperatures and inducer concentrations resulting in highest final production levels. When combined, yersiniabactin production was extended to ∼175 mg L-1 . © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1412-1417, 2016.
Robert Van Engelen - One of the best experts on this subject based on the ideXlab platform.
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ACM Southeast Regional Conference - Toward efficient flow-sensitive Induction Variable analysis and dependence testing for loop optimization
Proceedings of the 44th annual southeast regional conference on - ACM-SE 44, 2006Co-Authors: Yixin Shou, Johnnie Birch, Robert Van Engelen, Kyle A. GallivanAbstract:This paper presents a new approach to improve flow-sensitive Induction Variable analysis and data dependence testing on intermediate program representations, such as control-flow graphs with low-level operations representations in single static assignment forms. Current compiler techniques have difficulties optimizing loops that exhibit irregular control flow patterns. The inaccuracy of loop analysis results in conservative estimations of array-based data dependences in loops, which negatively affects the speedup of the loop via parallelization and vectorization. Our approach is based on a novel CR# (CR-sharp) algebra that effectively represents the value progressions of (conditionally) updated Variables in loops. The CR# forms of Induction Variables are constructed with a new flow-sensitive Induction Variable recognition algorithm. We also developed a new CR#-based nonlinear data dependence test that enables loops to be more effectively optimized.
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Value Range Analysis of Conditionally Updated Variables and Pointers
2004Co-Authors: Johnnie Birch, Robert Van Engelen, Kyle A. GallivanAbstract:Pointer-based array traversals are often used by C programmers to optimize sequential memory access. However, the use of pointer arithmetic prevents compilers from effectively applying dependence testing and loop analysis for optimization and parallelization. Because pointer Variables can be viewed as reference-type Induction Variables, conventional Induction Variable recognition and substitution methods can be used to translate pointer-based code to array-based code, thereby enabling the application of traditional loop restructuring compiler optimizations for parallelization. However, conditionally updated Induction Variables and pointers are prohibiting the application of these optimizations, due to the inability to determine closed-form function equivalents. This paper presents a method for value range analysis on conditionally updated Induction Variables and pointers which have no closed-form functions. The value range method determines the memory access patterns of pointers and arrays indexed with conditionally updated Induction Variables. Important applications of this method are in array bounds analysis and data dependence testing.
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ICS - A unified framework for nonlinear dependence testing and symbolic analysis
Proceedings of the 18th annual international conference on Supercomputing - ICS '04, 2004Co-Authors: Robert Van Engelen, Johnnie Birch, Yixin Shou, Burt Walsh, Kyle A. GallivanAbstract:This paper presents a unified approach for generalized Induction Variable recognition and substitution, pointer analysis, analysis of conditionally updated Variables, value range analysis, array region analysis, and nonlinear dependence testing. The analysis techniques share a well-defined uniform approach based on the chains of recurrences algebra. The uniform algebraic approach provides a powerful unified framework for developing analysis algorithms for restructuring compilers. The paper introduces a new set of analysis algorithms that accurately handle conditional control flow, pointer arithmetic, and nonlinear symbolic expressions in loops, which are known to be problematic for conventional restructuring compilers.
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The CR# Algebra and its Application in Loop Analysis and Optimization
2004Co-Authors: Robert Van EngelenAbstract:This report presents a novel family of linear-time algorithms for loop analysis based on the CR# (CR-sharp) algebra, which is a new nontrivial extension of the Chains of Recurrences (CR) algebra. Conventional compiler methods apply Induction Variable substitution and array recovery translations to construct closed forms for Induction Variables and pointers prior to dependence testing and loop optimization. In this report we take a radically different approach to symbolic analysis by turning the problem up-sidedown. We convert closed forms to recurrences and compute recurrence relations for (non)linear Induction Variables and conditionally updated Variables and pointers. The recurrence forms are used to solve a larger class of loop analysis problems such as nonlinear array dependence testing without requiring a-priori code translations.
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CC - Efficient Symbolic Analysis for Optimizing Compilers
Lecture Notes in Computer Science, 2001Co-Authors: Robert Van EngelenAbstract:Because most of the execution time of a program is typically spend in loops, loop optimization is the main target of optimizing and restructuring compilers. An accurate determination of Induction Variables and dependencies in loops is of paramount importance to many loop optimization and parallelization techniques, such as generalized loop strength reduction, loop parallelization by Induction Variable substitution, and loop-invariant expression elimination. In this paper we present a new method for Induction Variable recognition. Existing methods are either ad-hoc and not powerful enough to recognize some types of Induction Variables, or existing methods are powerful but not safe. The most powerful method known is the symbolic differencing method as demonstrated by the Parafrase-2 compiler on parallelizing the Perfect Benchmarks(R). However, symbolic differencing is inherently unsafe and a compiler that uses this method may produce incorrectly transformed programs without issuing a warning. In contrast, our method is safe, simpler to implement in a compiler, better adaptable for controlling loop transformations, and recognizes a larger class of Induction Variables.
Rudolf Eigenmann - One of the best experts on this subject based on the ideXlab platform.
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Interprocedural symbolic range propagation for optimizing compilers
Lecture Notes in Computer Science, 2006Co-Authors: Hansang Bae, Rudolf EigenmannAbstract:We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by Variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm's ability to help detect zero-trip loops for Induction Variable substitution and subscript ranges for array reductions.
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LCPC - Interprocedural Symbolic Range Propagation for Optimizing Compilers
Languages and Compilers for Parallel Computing, 2006Co-Authors: Hansang Bae, Rudolf EigenmannAbstract:We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by Variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm’s ability to help detect zero-trip loops for Induction Variable substitution and subscript ranges for array reductions.
Hansang Bae - One of the best experts on this subject based on the ideXlab platform.
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Interprocedural symbolic range propagation for optimizing compilers
Lecture Notes in Computer Science, 2006Co-Authors: Hansang Bae, Rudolf EigenmannAbstract:We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by Variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm's ability to help detect zero-trip loops for Induction Variable substitution and subscript ranges for array reductions.
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LCPC - Interprocedural Symbolic Range Propagation for Optimizing Compilers
Languages and Compilers for Parallel Computing, 2006Co-Authors: Hansang Bae, Rudolf EigenmannAbstract:We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by Variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm’s ability to help detect zero-trip loops for Induction Variable substitution and subscript ranges for array reductions.