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Energy Aware Lossless Data Compression

Energy Aware Lossless Data Compression. Kenneth Barr and Krste Asanović MIT Laboratory for Computer Science. Energy Aware Lossless Data Compression: Introduction . Motivation Compression can save wireless network energy. Observation Energy add < 1nJ Energy send > 1000nJ Approach

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Energy Aware Lossless Data Compression

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  1. Energy Aware Lossless Data Compression Kenneth Barr and Krste Asanović MIT Laboratory for Computer Science

  2. Energy Aware Lossless Data Compression:Introduction • Motivation • Compression can save wireless network energy. • Observation • Energyadd < 1nJ • Energysend > 1000nJ • Approach • Can we use 1000 “adds” to eliminate a bit? • Reconsider slow compressors that perform many operations to achieve best compression ratios? • Should we choose the fastest compressor because E=Pt? Kenneth Barr and Krste Asanovic – Mobisys 2003

  3. File size(network energy) Energy to achieve reduced file size Energy Aware Lossless Data Compression:Introduction • Results • There’s no easy answer. For minimum energy, one must characterize hardware, software, and workload. • Energy saved on Skiff • Compared to default (zlib-6): 31% (web) to 57% (text) savings • Asymmetric strategy can save 11%-12% percent over the best symmetric pair. Kenneth Barr and Krste Asanovic – Mobisys 2003

  4. Energy Aware Lossless Data Compression:Agenda • Experimental Setup • Hardware • Benchmarks • Observed Energy • Compression Applications • Compute, network, memory • Energy Analysis • What impacts compression energy? • Lowering Overall Energy of Transmission • Understanding cache behavior • Sleep mode affects choice • Asymmetric compression • Conclusion and Future Work Kenneth Barr and Krste Asanovic – Mobisys 2003

  5. Compaq Personal Server (aka Skiff) • CPU similar to iPAQ • Spread out and exposed to facilitate measurement • Network: Enterasys five volt 802.11b (Cardbus) Kenneth Barr and Krste Asanovic – Mobisys 2003

  6. Skiff enables power measurement Regulator (2V) • Measurement • Three power planes (after cutting traces) • PC-Card power measured with extender card • 2 measurements (supply voltage and current) per plane • 5 x 6.5sec samples at 60Hz sample rate; multimeter controlled via RS-232 • Error • Missed events are possible due to slow sample rate, but not a problem in practice • Error sources analyzed in a Compaq tech report • Total error (hardware + averaging): <1% • Higher error with simulation-based power estimation, but simulator is useful for instruction and event counts StrongARM V V 1 2 R SA-110 CPU cpu Regulator (3.3V) DRAM Mem. Controller R mem Flash Peripherals: wired ethernet, R Cardbus, RS232 peri Clocks, GPIO, et al. Regulator (5V) Wireless R ethernet card net 12V DC GND Kenneth Barr and Krste Asanovic – Mobisys 2003

  7. Benchmarks • Workload • 1MB English text from “Calgary Corpus” • A novel and structured bibliography • 1MB web data from most popular sites (according to “Lycos Top 50” searches and Neilsen Netratings) • No pre-compressed images (gif, jpg) were used • Mostly .html, .css, and .js • No sites had Java class files • Compressors • Represent major algorithms (LZ77, LZ78, PPM, BWT) • Chosen due to popularity, maturity, documentation, code quality, and portability • bzip2 (BWT) • Unix compress (LZ78) • LZO (“realtime” LZ77) • PPMd (PPM) • zlib (LZ77) Kenneth Barr and Krste Asanovic – Mobisys 2003

  8. Compressed request (HTTP GET, NFS Read, etc…) Compressed response (HTML Document, source code, etc…) Compression for portable devices • Goal: choose a compressor that strikes best balance between compressed file size (~ network energy) and time to achieve that size (~ compute energy) Portable Client Wall-powered Server Kenneth Barr and Krste Asanovic – Mobisys 2003

  9. Energy required to receive 1MB text • Receiving and uncompressing usually saves energy (compared to receiving uncompressed data) Kenneth Barr and Krste Asanovic – Mobisys 2003

  10. Energy required to send 1MB text • Compressing prior to sending can actually increase total energy! • Web data (not shown) is easier to compress and requires less energy than “none” for all except bzip2 Kenneth Barr and Krste Asanovic – Mobisys 2003

  11. Large effect of varying parameters • Parameters: size of input blocks, size of data structures, amount of effort • Use such a chart to choose best compressor for platform+data combo Kenneth Barr and Krste Asanovic – Mobisys 2003

  12. Energy per operation: Skiff • Microbenchmarks verify that computation is cheap… Kenneth Barr and Krste Asanovic – Mobisys 2003

  13. Instructions per bit • We don’t execute an unreasonable number of instructions (though there is quite a variation between applications!) Kenneth Barr and Krste Asanovic – Mobisys 2003

  14. Energy per operation: Skiff • Computation is cheap, cache misses are not. • By their nature, compressors can have many cache misses. Kenneth Barr and Krste Asanovic – Mobisys 2003

  15. Memory Footprints • Requiring many memory accesses leads to high energy • But a large memory footprint can be used wisely (eg, PPMd) Kenneth Barr and Krste Asanovic – Mobisys 2003

  16. Understanding cache behavior • Skiff cache is 16KB. No L2 Cache. • iPAQ cache only 8KB. Cache problems can be exacerbated. • X-Scale cache is 32KB. May still be a problem for apps tuned for the desktop • Suggestions for Unix Compress (which apply to other apps) • A 1K buffer speeds I/O, but cuts into 16KB cache • Not the size of allocation, it’s how you use it. (e.g., a large, sparse hash table  fewer collisions  fewer misses due to probing) • Merge adjacent tables into structure to bring in “code” with “fcode” : Original Merged Kenneth Barr and Krste Asanovic – Mobisys 2003

  17. struct entry{ signed fcode:20; unsigned code:12; }table[SIZE]; Understanding cache behavior • Suggestions (continued) • Compact structures to put more usable data in cache; less wasted space struct entry{ int fcode; unsigned short code; }table[SIZE]; Wasted space due to types and alignment padding Kenneth Barr and Krste Asanovic – Mobisys 2003

  18. Understanding cache behavior: results • Merging tables has little effect • Sparse arrays have dramatic effect even though logical table is much larger than cache • Compacting array removes 92% of cache misses from 11-merge • Not much energy left to be saved • But, program runs 1.5 times faster Kenneth Barr and Krste Asanovic – Mobisys 2003

  19. Compressed request (HTTP GET, NFS Read, etc…) Portable Client Compressed response (HTML Document, source code, etc… Wall-powered Server Asymmetric Compression • No need for the same compression method in both directions • Client compresses its requests using its lowest-energy compressor • Server supplies data (transcoding if necessary) so that client requires minimal energy to decompress • Server can maintain state for a flow as it may be hard to compress individual small blocks Kenneth Barr and Krste Asanovic – Mobisys 2003

  20. Overall results • Energy savings over mod_gzip default (eg compress12 vs zlib-6): • Text: 57% • Web: 31% • Asymmetric compression energy savings over best symmetric scheme(eg, compress12+zlib9 vs compress12+compress12) • Text: 11% • Web: 12% • Asymmetric energy savings over no compression • Text: 45% • Web: 73% Combination: Compressor + Decompressor Kenneth Barr and Krste Asanovic – Mobisys 2003

  21. Exploiting low-power sleep mode • Idle power will affect choice of compressor on unloaded processor • Low power idle? • Getting some work done quickly and going to sleep is best choice • High idle power? • It is best to spend time doing a good job otherwise platform wastes power while idle Kenneth Barr and Krste Asanovic – Mobisys 2003

  22. Total Energy as CPU and Memory Energy Decrease Total Energy as Network Energy Decreases 6.00 5.00 bzip2 4.00 compress • If network improves while CPU and memory remain constant? • Little change in choice • All files compress to same order of magnitude; energy dominated by CPU and memory of compressor Joules lzo 3.00 ppmd 2.00 zlib 1.00 0.00 9 8 7 6 5 4 3 2 1 0 10 Network Energy / CPU + Memory Energy Changing component energy affectschoice of compressor • If CPU and memory decrease in energy while network remains constant? • Aggressive compression becomes possible, if not better 6.00 5.00 bzip2 4.00 compress Joules 3.00 lzo ppmd 2.00 zlib 1.00 0.00 ∞ 10 12 13 15 17 21 26 35 52 105 Network Energy / Average CPU+Memory Energy Kenneth Barr and Krste Asanovic – Mobisys 2003

  23. Related work • Using sophisticated error correcting codes can reduce the number bits to send, but processing codes can outweigh the energy savings • Energy efficiency of error correction on wireless links (Havinga 1999) • Energy efficient lossy compression: recast the problem or trade energy for quality • CMU Odyssey(Satyanarayanan et al. 1994-2000) • Algorithmic transforms for efficient scalable computation(Sinha et al. 2000) • Adaptive image compression for wireless multimedia communication (Taylor and Dey 2001) • Recognize the importance of low-power idle mode • Critical power slope (Miyoshi et al. 2002) • Many other compression and optimization techniques • Several noted in my Master’s Thesis (Barr 2002) Kenneth Barr and Krste Asanovic – Mobisys 2003

  24. Conclusion & Future Work • Conclusions • Compression to save transmission energy is not always a net win. Default compressor can double send energy! • The fastest compressor is not always best; the smallest file is not always best. • However, knowledge of component energy and input data combined with wise choice of algorithms and parameters can give large energy savings: • Up to 57% over default scheme • Up to 12% over optimal symmetric scheme • Future work • Developing a hardware energy profiler for iPAQ that fits on a PC-Card to measure energy portably in an active system. Use its findings to choose best application or dynamically change. • Explore further implementation tweaks for cache-friendly behavior on portable systems. Kenneth Barr and Krste Asanovic – Mobisys 2003

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  26. Backup • Compression ratio? Text vs web? • See paper • Why not compress on the NIC? • Regardless, same set of tradeoffs • Higher bandwidth links -> less need. • Multiple flows mean less correlation • Better ratios at the application layer (application-specific compression can be employed, large context can be maintained). • Applications • Difficult for interactive or small packet traffic • If you have the choice over what format to receive (eg, bzip2? No!) • Room full of conference attendees sharing an access point Kenneth Barr and Krste Asanovic – Mobisys 2003

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