M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. Lecture Notes in Computer Science, 1222:263--??, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....a generic program, and not as the latest development in XML compression. Furthermore, we have not been able to obtain the executables or the source code of most of the existing XML compressors. Existing XML compressors. Structure specific compression methods give much better compression results [4, 15, 14, 46] than conventional compression methods such as the Unix compress utility [52] There exist many XML compressors; we know of XMLZip [12] XMill [36] ICT s XML Xpress [25] Millau [16] XMLPPM [6] XGrind [48] and lossy XML compression [5] We will not perform an exhaustive comparison between our ....

Michael Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Mobile Object Systems: Towards the Programmable Internet, pages 263--276. Springer-Verlag: Heidelberg, Germany, 1997.

....cache lines. Previous work in program compression has explored the compressibility of a wide range of program representations: source programs, intermediate representations, machine codes, etc. 24] The resulting compressed form either must be decompressed (and perhaps compiled) before execution [9, 10, 11] or it can be executed (or interpreted [13, 21] without decompression [6, 12] The first method results in a smaller compressed representation than the second, but requires the time and space overhead of decompression before execution. We avoid requiring a large amount of additional space to ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, LNCS vol. 1222, pp. 263--276. Springer, Feb. 1997.

....compression. Previous work in program compression has explored the compressiblity of a wide range of program representations: source languages, intermediate representations, machine codes, etc. 15] The resulting compressed form either must be decompressed (and perhaps compiled) before execution [5, 6, 7] or it can be executed (or interpreted [9, 14] without decompression [4, 8] The first method results in a smaller compressed representation than the second, but requires the overhead of decompression before execution. This overhead may be negligible and, in fact, may be compensated for by the ....

....e.g. for address computations for local arrays, such computations may be affected by changes in displacements within the stack frame. These problems are somewhat easier to handle if the procedural abstraction is being carried out before code generation, e.g. at the level of abstract syntax trees [6]. At the level of assembly code (as in the work of Cooper and McIntosh [4] and Fraser et al. 8] or machine code (as in our work) it becomes considerably more complicated. There are, however, some simple cases where it is possible to avoid the complications associated with having to save and ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. Technical Report 97-04, Department of Information and Computer Science, University of California, Irvine, February 1997.

....of the total memory footprint of the data structures required by other analyses in Squeeze. 8. RELATED WORK There is a considerable body of work on code compression, but much of this focuses on compressing executable les as much as possible in order to reduce storage or transmission costs [10, 11, 12, 13, 16, 17, 19]. These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [10, 11, 12, 13] which can be problematic for limited memory ....

.... to reduce storage or transmission costs [10, 11, 12, 13, 16, 17, 19] These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [10, 11, 12, 13] which can be problematic for limited memory devices or require special hardware support for executing the compressed code directly [16, 17] By contrast, programs compacted using our techniques can be executed directly without any decompression or special hardware support. Most of the previous ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, number 1222 in LNCS, pages 263-276. Springer, Feb. 1997.

....for the relative size of 1) code in the compacted binaries 2) data in the compacted binaries 3) code removed when applying code compaction only 4) additional code removed due to combined analysis and 5) data removed from the program. Each program is annotated with its full size in MB. sion costs [8, 9, 10, 11, 14, 15, 17]. These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [8, 9, 10, 11] which can be problematic for limited memory ....

....with its full size in MB. sion costs [8, 9, 10, 11, 14, 15, 17] These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [8, 9, 10, 11] which can be problematic for limited memory devices or require special hardware support for executing the compressed code directly [14, 15] By contrast, programs compacted using our techniques can be executed directly without any decompression or special hardware support. Most of the previous ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, number 1222 in LNCS, pages 263-276. Springer, Feb. 1997.

....the dynamically linked programs consist for a large part of a dynamic string and symbol table. 6. RELATED WORK There is a considerable body of work on code compression, but much of this focuses on compressing executable files as much as possible in order to reduce storage or transmission costs [8, 10, 11, 12, 14, 15, 17]. These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [8, 10, 11, 12] which can be problematic for limited memory ....

.... to reduce storage or transmission costs [8, 10, 11, 12, 14, 15, 17] These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [8, 10, 11, 12] which can be problematic for limited memory devices or require special hardware support for executing the compressed code directly [14, 15] By contrast, programs compacted using our techniques can be executed directly without any decompression or special hardware support. Most of the ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, number 1222 in LNCS, pages 263--276. Springer, Feb. 1997.

....rst time) To evaluate and compare these di erent approaches, Knabe ran benchmarks (e.g. an agent that sorts) and measured, in every case, the time for marshaling, transferring, unmarshaling, compiling, and executing an MA. Franz has developed a compressed syntax tree representation of MA code [7]. MA code is generated at run time from the compressed syntax tree representation. The aim is to reduce transfer time and augment retention of MA code in cache memory. Franz evaluated his approach by comparing the size of MA code in his representation with the size of MA code in other known ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Vitek and Tschudin [22], pages 229-243.

....to the code and data compaction, but also to the removal of unnecessary stack spills by Squeeze. 8. RELATED WORK There is a considerable body of work on code compression, but much of this focuses on compressing executable les as much as possible in order to reduce storage or transmission costs [5, 6, 7, 8, 11, 12]. These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [5, 6, 7, 8] which can be problematic for limited memory devices or ....

.... order to reduce storage or transmission costs [5, 6, 7, 8, 11, 12] These approaches generally produce compressed representables that are smaller than those obtained using our approach, but have the drawback that they must either be decompressed to their original size before they can be executed [5, 6, 7, 8] which can be problematic for limited memory devices or require special base code compacted code and data compaction program text binary text binary text binary 164.gzip 57592 318592 30464 (52.8 ) 228480 (71.7 ) 29472 (51.2 ) 211888 (66.5 ) 175.vpr 100108 542544 62912 (62.8 ) 411472 (75.8 ) ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, number 1222 in LNCS, pages 263-276. Springer, Feb. 1997.

....e.g. for address computations for local arrays, such computations may be affected by changes in displacements within the stack frame. These problems are somewhat easier to handle if the procedural abstraction is being carried out before code generation, e.g. at the level of abstract syntax trees [32]. At the level of assembly code [21, 35] or machine code (as in our work) it becomes considerably more complicated. There are, however, some simple cases where it is possible to avoid the complications associated with having to save and restore the return address when introducing procedural ....

Michael Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Mobile Object Systems: Towards the Programmable Internet, pages 263--276. SpringerVerlag, April 1997. Lecture Notes in Computer Science No. 1222.

....Previous work in reducing program size has explored the compressiblity of a wide range of program representations: source languages, intermediate representations, machine codes, etc. 18] The resulting compressed form either must be decompressed (and perhaps compiled) before execution [6, 7, 8] or it can be executed (or interpreted [11, 17] without decompression [5, 10] The rst method results in a smaller compressed representation than the second, but requires the overhead of decompression before execution. Decompression time may be negligible and, in fact, may be compensated for by ....

....e.g. for address computations for local arrays, such computations may be a ected by changes in displacements within the stack frame. These problems are somewhat easier to handle if the procedural abstraction is being carried out before code generation, e.g. at the level of abstract syntax trees [7]. At the level of assembly code [5, 10] or machine code (as in our work) it becomes considerably more complicated. There are, however, some simple cases where it is possible to avoid the complications associated with having to save and restore the return address when introducing procedural ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. in Mobile Object Systems: Towards the Programmable Internet, eds. J. Vitek and C. Tschudin, Springer LNCS vol. 1222, pp. 263-276, Feb. 1997.

....compression. Previous work in program compression has explored the compressiblity of a wide range of program representations: source languages, intermediate representations, machine codes, etc. 16] The resulting compressed form either must be decompressed (and perhaps compiled) before execution [5, 6, 7] or it can be executed (or interpreted [10, 15] without decompression [4, 9] The rst method results in a smaller compressed representation than the second, but requires the overhead of decompression before execution. This overhead may be negligible and, in fact, may be compensated for by the ....

....e.g. for address computations for local arrays, such computations may be a ected by changes in displacements within the stack frame. These problems are somewhat easier to handle if the procedural abstraction is being carried out before code generation, e.g. at the level of abstract syntax trees [6]. At the level of assembly code [4, 9] or machine code (as in our work) it becomes considerably more complicated. There are, however, some simple cases where it is possible to avoid the complications associated with having to save and restore the return address when introducing procedural ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. Technical Report 97-04, Department of Information and Computer Science, University of California, Irvine, February 1997.

....than Java, so a direct comparison is still a matter of future work. In the earlier work, we compressed bytecode like program representations to 20 of their original size. The compression of an abstract syntax tree representation of a program has been studied in some detail by Michael Franz [7]. This representation is similar to a parse tree representation 7 0 0.05 0.1 0.15 0.2 0.25 JDK JavaCup JLex Toba Gzip LALR LR TopD 175,153 112,286 119,368 267,133 Table 4: Compression of Java using bottom up (LR and LALR) and top down guided parsing. The bars are grouped by parsing ....

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. Technical Report 97-04, Department of Information and Computer Science, University of California, Irvine, February 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. Lecture Notes in Computer Science, 1222:263--??, 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. Lecture Notes in Computer Science, 1222:263--276, 1997.

No context found.

Michael Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Mobile Object Systems: Towards the Programmable Internet, pages 263--276. Springer-Verlag: Heidelberg, Germany, 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. Lecture Notes in Computer Science, 1222:263--??, 1997.

No context found.

Michael Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Mobile Object Systems: Towards the Programmable Internet, pages 263--276. Springer-Verlag: Heidelberg, Germany, 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. In J. Vitek and C. Tschudin, editors, Mobile Object Systems: Towards the Programmable Internet, number 1222 in LNCS, pages 263--276. Springer, Feb. 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. Lecture Notes in Computer Science, 1222:263--276, 1997.

No context found.

Michael Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile object systems. In Mobile Object Systems: Towards the Programmable Internet, pages 263--276. Springer-Verlag: Heidelberg, Germany, 1997.

No context found.

M. Franz. Adaptive compression of syntax trees and iterative dynamic code optimization: Two basic technologies for mobile-object systems. TR 97-04, Dept of Information and Computer Science, University of California, Irvine, 2/97.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC