Fast Double-Precision Data Compression for High Throughput
"Learn about high-throughput compression of double-precision floating-point data using innovative algorithms to reduce data size, speed up transfer, and optimize processing. Explore how this approach addresses challenges in compressing floating-point data efficiently." (275 characters)
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Presentation Transcript
High Throughput Compression of Double-Precision Floating-Point Data Martin Burtscher and Paruj Ratanaworabhan School of Electrical and Computer Engineering Cornell University
Introduction Scientific programs Produce and transfer lots of 64-bit FP data Exchange 100s of MB/s, generate 1TB/day of new data Large amounts of data Are expensive to store and transfer Take a long time to transfer Data compression Can reduce amount of data Can speed up transfer Fast Floating-Point Compression March 2007
IEEE 754 Double-Precision Values Goal Compress linear streams of FP data fast and well Online operation and lossless compression Challenges Floating-point data are hard to compress FP codes may generate over 90% unique values Related work on lossless FP compression Focuses on 32-bit single-precision values Relies on smoothness of data or known geometry Fast Floating-Point Compression March 2007
Floating-Point Data Compression Our approach Predict FP data with value prediction algorithms and encode the difference Format: S Exponent 63 62 52 51 0 Mantissa Value predictors Hardware devices to speed up processors Predict instruction result by extrapolating previously sequences of computed results Employ very fast and simple algorithms Fast Floating-Point Compression March 2007
FPC Algorithm uncompressed 1D stream of doubles double . . . . . . 3f82 3b1e 0e32 f39d 64 Make two predictions Select closer value XOR with true value Count leading zeros Encode value Update predictors FCM DFCM 64 64 3f82 4 3f51 9 compare compare selector predictor code 1 closer value 64 XOR leading zero byte counter encoder 1+3 0 to 8 bytes remainderb 7129 889b 0e5d bita cnta x y bitb cntb 0 2 remaindera z compressed stream . . . . . . Fast Floating-Point Compression March 2007
Algorithm/Implementation Co-Design Inner loop (about 50 and 70 C statements) Compresses or decompresses one block of data Accounts for over 90% of execution time Loop body optimizations Loop body is used to hide memory latency No fp, int mult, or int div instructions No branches (only conditional moves) Single basic block (>100 machine instructions) Average IPC > 5.4 and 5.1 on Itanium 2 Fast Floating-Point Compression March 2007
Evaluation Method System 1.6 GHz Itanium 2, Intel C Itanium Compiler 9.1 Red Hat Enterprise Linux AS4 Scientific datasets Linear streams of 64-bit FP data (18 277MB) 4 observations: spitzer, temp, error, info 4 simulations: comet, plasma, brain, control 5 messages: bt, lu, sp, sppm, sweep3d Fast Floating-Point Compression March 2007
Compression Throughput 6 BZIP2 GZIP PLMI FSD DFCM FPC 5 compression throughput (Gb/s) 4 3 2 1 0 1.0 1.1 1.2 1.3 1.4 compression ratio 1.5 1.6 1.7 1.8 1.9 2.0 Fast Floating-Point Compression March 2007
Decompression Throughput 7 BZIP2 DFCM FPC FSD GZIP PLMI 6 decompression throughput (Gb/s) 5 4 3 2 1 0 1.0 1.1 1.2 1.3 1.4 compression ratio 1.5 1.6 1.7 1.8 1.9 2.0 Fast Floating-Point Compression March 2007
Summary and Conclusions FPC algorithm Highest throughput and mean compression ratio 1.02 15.05 absolute compression ratio 840 and 680 MB/s throughput on a 1.6GHz Itanium 2 (= 2 and 2.5 machine cycles per byte) http://www.csl.cornell.edu/~burtscher/research/FPC/ Conclusions Value predictors are fast & accurate data models Algorithm/implementation co-design is essential Fast Floating-Point Compression March 2007
