Vertical Object Layout and Compression for Fixed Heaps

Titzer, Ben L.; Palsberg, Jens

doi:10.1007/978-3-642-04164-8_18

Cited by 1 publication

(2 citation statements)

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“…Stephenson et al use static analysis to perform bit-width compression on C programs before compiling those C programs to FPGAs [24]. Cooprider and Regehr [10], Titzer [27], and Titzer and Palsberg [28] compress executable images for embedded chips. They apply bit-width compression and field value set indirection on pre-allocated static data, but not on dynamically allocated heap data.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Ananian and Rinard use profiling and static analysis to implement approaches such as constant field elision and bit-width reduction [2]. Others explore approaches for making other programming languages more memory efficient [3,10,12,24,26,27,28,29]. For example, Appel and Gonçalves share memory between equivalent SML objects using the garbage collector [3].…”

Section: Introductionmentioning

confidence: 99%

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No bit left behind

Sartor

Hirzel

McKinley

2008

Proceedings of the 7th International Symposium on Memory Management

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On one hand, the high cost of memory continues to drive demand for memory efficiency on embedded and general purpose computers. On the other hand, programmers are increasingly turning to managed languages like Java for their functionality, programmability, and reliability. Managed languages, however, are not known for their memory efficiency, creating a tension between productivity and performance. This paper examines the sources and types of memory inefficiencies in a set of Java benchmarks. Although prior work has proposed specific heap data compression techniques, they are typically restricted to one model of inefficiency. This paper generalizes and quantitatively compares previously proposed memorysaving approaches and idealized heap compaction. It evaluates a variety of models based on strict and deep object equality, field value equality, removing bytes that are zero, and compressing fields and arrays with a limited number and range of values. The results show that substantial memory reductions are possible in the Java heap. For example, removing bytes that are zero from arrays is particularly effective, reducing the application's memory footprint by 41% on average. We are the first to combine multiple savings models on the heap, which very effectively reduces the application by up to 86%, on average 58%. These results demonstrate that future work should be able to combine a high productivity programming language with memory efficiency.

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Section: Related Workmentioning

confidence: 99%