The Experts below are selected from a list of 4887 Experts worldwide ranked by ideXlab platform
Jiong Hong - One of the best experts on this subject based on the ideXlab platform.
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Effective Optimization of Antibody Affinity by Phage Display Integrated with High-Throughput DNA Synthesis and Sequencing Technologies
PLOS ONE, 2015Co-Authors: Dongmei Hu, Ruikai Du, Man Xu, Siyi Hu, Wei Zhao, Jiong HongAbstract:Phage display technology has been widely used for antibody affinity maturation for decades. The limited library sequence diversity together with excessive redundancy and labour-consuming procedure for candidate identification are two major obstacles to widespread adoption of this technology. We hereby describe a novel library generation and screening approach to address the problems. The approach started with the targeted diversification of multiple complementarity determining regions (CDRs) of a humanized anti-ErbB2 antibody, HuA21, with a small perturbation mutagenesis strategy. A combination of three degenerate codons, NWG, NWC, and NSG, were chosen for amino acid saturation mutagenesis without introducing cysteine and stop residues. In total, 7,749 degenerate oligonucleotides were synthesized on two microchips and released to construct five single-chain antibody fragment (scFv) gene libraries with 4 x 106 DNA sequences. Deep sequencing of the unselected and selected phage libraries using the Illumina platform allowed for an in-depth evaluation of the enrichment landscapes in CDR sequences and amino acid substitutions. Potent candidates were identified according to their high frequencies using NGS analysis, by-passing the need for the primary screening of target-binding clones. Furthermore, a subsequent library by recombination of the 10 most abundant variants from four CDRs was constructed and screened, and a mutant with 158-fold increased affinity (Kd = 25.5 pM) was obtained. These results suggest the potential application of the developed methodology for optimizing the binding properties of other antibodies and biomolecules.
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Effective Optimization of antibody affinity by phage display integrated with high-throughput DNA synthesis and sequencing technologies
PLoS ONE, 2015Co-Authors: Dongmei Hu, Wen Wan, Ruikai Du, Haiyan Liu, Xiaolian Gao, Man Xu, Siyi Hu, Jing Liu, Wei Zhao, Jiong HongAbstract:© 2015 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Phage display technology has been widely used for antibody affinity maturation for decades. The limited library sequence diversity together with excessive redundancy and labour-consuming procedure for candidate identification are two major obstacles to widespread adoption of this technology. We hereby describe a novel library generation and screening approach to address the problems. The approach started with the targeted diversification of multiple complementarity determining regions (CDRs) of a humanized anti-ErbB2 antibody, HuA21, with a small perturbation mutagenesis strategy. A combination of three degenerate codons, NWG, NWC, and NSG, were chosen for amino acid saturation mutagenesis without introducing cysteine and stop residues. In total, 7,749 degenerate oligonucleotides were synthesized on two microchips and released to construct five singlechain antibody fragment (scFv) gene libraries with 4 × 106 DNA sequences. Deep sequencing of the unselected and selected phage libraries using the Illumina platform allowed for an in-depth evaluation of the enrichment landscapes in CDR sequences and amino acid substitutions. Potent candidates were identified according to their high frequencies using NGS analysis, by-passing the need for the primary screening of target-binding clones. Furthermore, a subsequent library by recombination of the 10 most abundant variants from four CDRs was constructed and screened, and a mutant with 158-fold increased affinity (Kd = 25.5 pM) was obtained. These results suggest the potential application of the developed methodology for optimizing the binding properties of other antibodies and biomolecules.
Prasad A Kulkarni - One of the best experts on this subject based on the ideXlab platform.
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Fast and efficient searches for Effective Optimization-phase sequences
ACM Transactions on Architecture and Code Optimization, 2005Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and possibly executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where meeting strict constraints on execution time, code size, and power consumption is paramount and longer compilation times may be tolerated in the final stage of development, when an application is compiled one last time and embedded in a product. Unfortunately, even for small embedded applications, the search process can take many hours or even days making the approach less attractive to developers. In this paper, we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 62p, on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show this approach decreases the average number of required generations by 59p. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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fast searches for Effective Optimization phase sequences
Programming Language Design and Implementation, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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PLDI - Fast searches for Effective Optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation - PLDI '04, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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finding Effective Optimization phase sequences
Languages Compilers and Tools for Embedded Systems, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
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LCTES - Finding Effective Optimization phase sequences
ACM SIGPLAN Notices, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
Dongmei Hu - One of the best experts on this subject based on the ideXlab platform.
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Effective Optimization of Antibody Affinity by Phage Display Integrated with High-Throughput DNA Synthesis and Sequencing Technologies
PLOS ONE, 2015Co-Authors: Dongmei Hu, Ruikai Du, Man Xu, Siyi Hu, Wei Zhao, Jiong HongAbstract:Phage display technology has been widely used for antibody affinity maturation for decades. The limited library sequence diversity together with excessive redundancy and labour-consuming procedure for candidate identification are two major obstacles to widespread adoption of this technology. We hereby describe a novel library generation and screening approach to address the problems. The approach started with the targeted diversification of multiple complementarity determining regions (CDRs) of a humanized anti-ErbB2 antibody, HuA21, with a small perturbation mutagenesis strategy. A combination of three degenerate codons, NWG, NWC, and NSG, were chosen for amino acid saturation mutagenesis without introducing cysteine and stop residues. In total, 7,749 degenerate oligonucleotides were synthesized on two microchips and released to construct five single-chain antibody fragment (scFv) gene libraries with 4 x 106 DNA sequences. Deep sequencing of the unselected and selected phage libraries using the Illumina platform allowed for an in-depth evaluation of the enrichment landscapes in CDR sequences and amino acid substitutions. Potent candidates were identified according to their high frequencies using NGS analysis, by-passing the need for the primary screening of target-binding clones. Furthermore, a subsequent library by recombination of the 10 most abundant variants from four CDRs was constructed and screened, and a mutant with 158-fold increased affinity (Kd = 25.5 pM) was obtained. These results suggest the potential application of the developed methodology for optimizing the binding properties of other antibodies and biomolecules.
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Effective Optimization of antibody affinity by phage display integrated with high-throughput DNA synthesis and sequencing technologies
PLoS ONE, 2015Co-Authors: Dongmei Hu, Wen Wan, Ruikai Du, Haiyan Liu, Xiaolian Gao, Man Xu, Siyi Hu, Jing Liu, Wei Zhao, Jiong HongAbstract:© 2015 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Phage display technology has been widely used for antibody affinity maturation for decades. The limited library sequence diversity together with excessive redundancy and labour-consuming procedure for candidate identification are two major obstacles to widespread adoption of this technology. We hereby describe a novel library generation and screening approach to address the problems. The approach started with the targeted diversification of multiple complementarity determining regions (CDRs) of a humanized anti-ErbB2 antibody, HuA21, with a small perturbation mutagenesis strategy. A combination of three degenerate codons, NWG, NWC, and NSG, were chosen for amino acid saturation mutagenesis without introducing cysteine and stop residues. In total, 7,749 degenerate oligonucleotides were synthesized on two microchips and released to construct five singlechain antibody fragment (scFv) gene libraries with 4 × 106 DNA sequences. Deep sequencing of the unselected and selected phage libraries using the Illumina platform allowed for an in-depth evaluation of the enrichment landscapes in CDR sequences and amino acid substitutions. Potent candidates were identified according to their high frequencies using NGS analysis, by-passing the need for the primary screening of target-binding clones. Furthermore, a subsequent library by recombination of the 10 most abundant variants from four CDRs was constructed and screened, and a mutant with 158-fold increased affinity (Kd = 25.5 pM) was obtained. These results suggest the potential application of the developed methodology for optimizing the binding properties of other antibodies and biomolecules.
Jack W Davidson - One of the best experts on this subject based on the ideXlab platform.
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Fast and efficient searches for Effective Optimization-phase sequences
ACM Transactions on Architecture and Code Optimization, 2005Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and possibly executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where meeting strict constraints on execution time, code size, and power consumption is paramount and longer compilation times may be tolerated in the final stage of development, when an application is compiled one last time and embedded in a product. Unfortunately, even for small embedded applications, the search process can take many hours or even days making the approach less attractive to developers. In this paper, we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 62p, on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show this approach decreases the average number of required generations by 59p. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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fast searches for Effective Optimization phase sequences
Programming Language Design and Implementation, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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PLDI - Fast searches for Effective Optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation - PLDI '04, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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finding Effective Optimization phase sequences
Languages Compilers and Tools for Embedded Systems, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
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LCTES - Finding Effective Optimization phase sequences
ACM SIGPLAN Notices, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
David Whalley - One of the best experts on this subject based on the ideXlab platform.
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Fast and efficient searches for Effective Optimization-phase sequences
ACM Transactions on Architecture and Code Optimization, 2005Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and possibly executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where meeting strict constraints on execution time, code size, and power consumption is paramount and longer compilation times may be tolerated in the final stage of development, when an application is compiled one last time and embedded in a product. Unfortunately, even for small embedded applications, the search process can take many hours or even days making the approach less attractive to developers. In this paper, we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 62p, on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show this approach decreases the average number of required generations by 59p. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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fast searches for Effective Optimization phase sequences
Programming Language Design and Implementation, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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PLDI - Fast searches for Effective Optimization phase sequences
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation - PLDI '04, 2004Co-Authors: Prasad A Kulkarni, David Whalley, Jack W Davidson, Stephen Hines, Jason D Hiser, Douglas L JonesAbstract:It has long been known that a fixed ordering of Optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and executed to evaluate each sequence's Effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where longer compilation times may be tolerated in the final stage of development. In this paper we describe two complementary general approaches for achieving faster searches for Effective Optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 65% on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show that the average number of required generations decreased by 68%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
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finding Effective Optimization phase sequences
Languages Compilers and Tools for Embedded Systems, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
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LCTES - Finding Effective Optimization phase sequences
ACM SIGPLAN Notices, 2003Co-Authors: Prasad A Kulkarni, Wankang Zhao, Hwashin Moon, David Whalley, Jack W Davidson, Mark W Bailey, Yunheung Paek, Kyle A GallivanAbstract:It has long been known that a single ordering of Optimization phases will not produce the best code for every application. This phase ordering problem can be more severe when generating code for embedded systems due to the need to meet conflicting constraints on time, code size, and power consumption. Given that many embedded application developers are willing to spend time tuning an application, we believe a viable approach is to allow the developer to steer the process of optimizing a function. In this paper, we describe support in VISTA, an interactive compilation system, for finding Effective sequences of Optimization phases. VISTA provides the user with dynamic and static performance information that can be used during an interactive compilation session to gauge the progress of improving the code. In addition, VISTA provides support for automatically using performance information to select the best Optimization sequence among several attempted. One such feature is the use of a genetic algorithm to search for the most efficient sequence based on specified fitness criteria. We have included a number of experimental results that evaluate the Effectiveness of using a genetic algorithm in VISTA to find Effective Optimization phase sequences.
