التفاصيل البيبلوغرافية
| العنوان: |
Compressed linear algebra for declarative large-scale machine learning. |
| المؤلفون: |
Elgohary, Ahmed, Boehm, Matthias, Haas, Peter J., Reiss, Frederick R., Reinwald, Berthold |
| المصدر: |
Communications of the ACM; May2019, Vol. 62 Issue 5, p83-91, 9p, 5 Diagrams, 2 Charts, 4 Graphs |
| مصطلحات موضوعية: |
LINEAR algebra, MACHINE learning, READ-only memory, LOSSLESS data compression, MATRICES (Mathematics), ALGORITHMS |
| مستخلص: |
Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to enable fast matrix-vector operations. General-purpose compression struggles to achieve both good compression ratios and fast decompression for block-wise uncompressed operations. Therefore, we introduce Compressed Linear Algebra (CLA) for lossless matrix compression. CLA encodes matrices with lightweight, value-based compression techniques and executes linear algebra operations directly on the compressed representations. We contribute effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Our experiments show good compression ratios and operations performance close to the uncompressed case, which enables fitting larger datasets into available memory. We thereby obtain significant end-to-end performance improvements. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |