Compiler techniques for code compaction

Debray, Saumya; Evans, William S.; Muth, Robert; Sutter, Bjorn De

doi:10.1145/349214.349233

Cited by 199 publications

(135 citation statements)

References 2 publications

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“…Previous works include a wide range of techniques from code compression [10][11][12][13] to procedure abstraction [14][15][16] and dead code elimination [17].…”

Section: Related Workmentioning

confidence: 99%

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Profile-Driven Selective Program Loading

Ince

Hollingsworth

2010

Euro-Par 2010 - Parallel Processing

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Abstract. Complex software systems use many shared libraries frequently composed of large off-the-shelf components. Only a limited number of functions are used from these shared libraries. Historically demand paging prevented this from wasting large amounts of memory. Many high end systems lack virtual memory and thus must load the entire shared library into each node's memory. In this paper we propose a system which decreases the memory footprint of applications by selectively loading only the used portions of the shared libraries. After profiling executables and shared libraries, our system rewrites all target shared libraries with a new function ordering and updated ELF program headers so that the loader only loads those functions that are likely to be used by a given application and includes a fallback user-level paging system to recover in the case of failures in our analysis. We present a case study that shows our system achieves more than 80% reduction in the number of pages that are loaded for several HPC applications while causing no performance overhead for reasonably long running programs.

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“…Previous works include a wide range of techniques from code compression [10][11][12][13] to procedure abstraction [14][15][16] and dead code elimination [17].…”

Section: Related Workmentioning

confidence: 99%

“…[16,17] explain various code elimination methods including unreachable, redundant and dead code elimination. These methods are demonstrated in their binary-rewriting tool, squeeze, along with interprocedural constant propagation and strength reduction.…”

Section: Related Workmentioning

confidence: 99%

Profile-Driven Selective Program Loading

Ince

Hollingsworth

2010

Euro-Par 2010 - Parallel Processing

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“…Upon execution of a stub, the necessary code is decompressed in a fixed-size buffer. When applied to programs that were already optimized for code size using link-time compaction techniques [12], additional code size reductions of 13.7% to 18.8% were achieved. The performance impact ranges from a slight speedup to a 28% slowdown.…”

Section: On-demand Code Loadingmentioning

confidence: 99%

Linux Kernel Compaction through Cold Code Swapping

Chanet

Cabezas

Morancho

et al. 2009

Transactions on High-Performance Embedded Architectures and Compilers II

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Abstract. There is a growing trend to use general-purpose operating systems like Linux in embedded systems. Previous research focused on using compaction and specialization techniques to adapt a general-purpose OS to the memory-constrained environment presented by most embedded systems. However, there is still room for improvement: it has been shown that even after application of the aforementioned techniques more than 50% of the kernel code remains unexecuted under normal system operation. We introduce a new technique that reduces the Linux kernel code memory footprint through on-demand code loading of infrequently executed code, for systems that support virtual memory. In this paper, we describe our general approach, and we study code placement algorithms to minimize the performance impact of the code loading. A code size reduction of 68% is achieved, with a 2.2% execution speedup of the system-mode execution time, for a case study based on the MediaBench II benchmark suite.

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“…As the microcode is mostly hand-coded, we may assume that the best effort to remove redundancy by means of software has already been applied. These range from the use of classical compiler optimizations such as strength reduction, dead code elimination, tail merging, and common sub-expression elimination [6] to more elaborated techniques, like Procedural Abstraction [8], [16].…”

Section: Related Workmentioning

confidence: 99%

Clustering-Based Microcode Compression

Borin

Breternitz

et al. 2006

2006 International Conference on Computer Design

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Abstract-Microcode enables programmability of (micro) architectural structures to enhance functionality and to apply patches to an existing design. As more features get added to a CPU core, the area and power costs associated with microcode increase. A recent Intel internal design targeted at low power and small footprint has estimated the costs of the microcode ROM to approach 20% of the total die area (and associated power consumption). Therefore, it is desirable to apply compression techniques to microcode.Microcode poses unique challenges for compression due to the long instruction format, the hand-coded nature of the programs and the stringent performance requirements that require fast decompression. This paper describes techniques for microcode compression that achieve significant area and power savings, while presenting a streamlined architecture that enables high throughput within the constraints of a high performance CPU. The paper presents results for microcode compression on several commercial CPU designs which demonstrates compression ratios ranging from 50% to 62%.

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Compiler techniques for code compaction

Cited by 199 publications

References 2 publications

Profile-Driven Selective Program Loading

Profile-Driven Selective Program Loading

Linux Kernel Compaction through Cold Code Swapping

Clustering-Based Microcode Compression

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