The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Arie Setya Putra - One of the best experts on this subject based on the ideXlab platform.
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Paperplain Fundamental Create Application With Borland Delphi 7.0 University Of Mitra Indonesia
2018Co-Authors: Arie Setya PutraAbstract:This paperplain is one of media in faculty of computer in University of Mitra Indonesia in class Programming Basicly in Borland Delphi 7.0 with Lecture Arie Setya Putra
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Paperplain: Execution Fundamental Create Application With Borland Delphi 7.0 University Of Mitra Indonesia
2018Co-Authors: Arie Setya PutraAbstract:This paperplain is one of media in faculty of computer in university of Mitra Indonesia in class Programming Basicly in Borland Delphi 7.0 with Lecture Arie Setya Putra With Series Code Of Document :AJULE-001 2018
Hironori Kasahara - One of the best experts on this subject based on the ideXlab platform.
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LCPC - Automatic Local Memory Management for Multicores Having Global Address Space
Languages and Compilers for Parallel Computing, 2017Co-Authors: Kouhei Yamamoto, Tomoya Shirakawa, Akimasa Yoshida, Keiji Kimura, Hironori KasaharaAbstract:Embedded multicore processors for hard real-time Applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to Create Application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multi-grain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific Applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.
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automatic local memory management for multicores having global address space
Languages and Compilers for Parallel Computing, 2016Co-Authors: Kouhei Yamamoto, Tomoya Shirakawa, Akimasa Yoshida, Keiji Kimura, Hironori KasaharaAbstract:Embedded multicore processors for hard real-time Applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to Create Application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multi-grain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific Applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.
Skom Chandra - One of the best experts on this subject based on the ideXlab platform.
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NATION CULTURAL RECOGNITION ApplicationS USING MICROSOFT VISUAL BASIC 6.0
Information Systems, 2010Co-Authors: Siska Wulandari, Skom ChandraAbstract:The growing world of computer technology so that all problems can be solved with a computer, one of which is to Create "Application Introduction to Cultural Nation", where with this one can obtain information from the local culture in Indonesia and is designed with the view that considering the ease in in operation. This Application can be run on a computer that is running Windows-based operating system.
Xia Tian - One of the best experts on this subject based on the ideXlab platform.
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Development Technology Based on EJB Components
Computer Engineering, 2002Co-Authors: Xia TianAbstract:Enterprise Java Beans(EJB)components architecture is designed for reusable Application over server-side, and it can Create ,Application program which is secure and running on multi-platform. This article describes EJB model and its propertiesand an applixation example of EBJ technology is also presented.;;
Akimasa Yoshida - One of the best experts on this subject based on the ideXlab platform.
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LCPC - Automatic Local Memory Management for Multicores Having Global Address Space
Languages and Compilers for Parallel Computing, 2017Co-Authors: Kouhei Yamamoto, Tomoya Shirakawa, Akimasa Yoshida, Keiji Kimura, Hironori KasaharaAbstract:Embedded multicore processors for hard real-time Applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to Create Application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multi-grain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific Applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.
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automatic local memory management for multicores having global address space
Languages and Compilers for Parallel Computing, 2016Co-Authors: Kouhei Yamamoto, Tomoya Shirakawa, Akimasa Yoshida, Keiji Kimura, Hironori KasaharaAbstract:Embedded multicore processors for hard real-time Applications like automobile engine control require the usage of local memory on each processor core to precisely meet the real-time deadline constraints, since cache memory cannot satisfy the deadline requirements due to cache misses. To utilize local memory, programmers or compilers need to explicitly manage data movement and data replacement for local memory considering the limited size. However, such management is extremely difficult and time consuming for programmers. This paper proposes an automatic local memory management method by compilers through (i) multi-dimensional data decomposition techniques to fit working sets onto limited size local memory (ii) suitable block management structures, called Adjustable Blocks, to Create Application specific fixed size data transfer blocks (iii) multi-dimensional templates to preserve the original multi-dimensional representations of the decomposed multi-dimensional data that are mapped onto one-dimensional Adjustable Blocks (iv) block replacement policies from liveness analysis of the decomposed data, and (v) code size reduction schemes to generate shorter codes. The proposed local memory management method is implemented on the OSCAR multi-grain and multi-platform compiler and evaluated on the Renesas RP2 8 core embedded homogeneous multicore processor equipped with local and shared memory. Evaluations on 5 programs including multimedia and scientific Applications show promising results. For instance, speedups on 8 cores compared to single core execution using off-chip shared memory on an AAC encoder program, a MPEG2 encoder program, Tomcatv, and Swim are improved from 7.14 to 20.12, 1.97 to 7.59, 5.73 to 7.38, and 7.40 to 11.30, respectively, when using local memory with the proposed method. These evaluations indicate the usefulness and the validity of the proposed local memory management method on real embedded multicore processors.