Efficient Memory Footprint Reduction for Java Performance

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Our prior work on data compression significantly advanced this subfield of computer architecture, and also showed the significant potential of applying optimized data compression across the whole systems stack (from caches to memory). As it commonly happens, our results also highlighted some completely new problems and opportunities. First of all, to be efficient, data compression needs to be very fast, especially decompression part as it is frequently on the critical execution path. Second, compressibility varies a lot for different applications, but still is generic enough to be interesting and is observed in 60% to 75% of different applications for both CPU and GPGPU applications. Third, to be efficient in memory hierarchy, it is better to use domain-specific or pattern-specific compression algorithms that might sacrifice some compression ratio for acceptable overhead of compression/decompression. In this work, we aim to apply our experience on data compression to a new area where the current application of data compression was very limited so far - just-in-time (JIT) compilation. Based on our initial discussion, there is a strong indication that memory footprint is an important concern for Java performance. In our project, we aim to address this problem using data compression. We envision several major directions in our research. First, we would like to collect a set of representative Java applications and generate their memory dumps that would include both the JIT metadata, compiled objects, and the heap. Second, we will perform an extensive analysis of existing compression algorithms that might be useful for each for the major contributors to the memory consumption. Third, we will look for potential domain-specific encodings that will improve compressibility of Java applications even further. Fourth, we will investigate a potential of running execution directly on compressed data when possible.