Abstract: The speed of dictionary based decompression is limited by the cost of accessing random values in the dictionary. If the size of the dictionary can be limited so it fits into cache, decompression is made to be CPU bound rather than memory bound. To achieve this, a value prefix coding scheme is presented, wherein value prefixes are stored in the dictionary to get good compression from small dictionaries. Also presented is an algorithm that determines the optimal entries for a value prefix dictionary. Once the dictionary fits in cache, decompression speed is often limited by the cost of mispredicted branches during Huffman code processing. A novel way is presented to quantize Huffman code lengths to allow code processing to be performed with few instructions, no branches, and very little extra memory. Also presented is an algorithm for code length quantization that produces the optimal assignment of Huffman codes and show that the adverse effect of quantization on the compression ratio is quite small.

Abstract: The speed of dictionary based decompression is limited by the cost of accessing random values in the dictionary. If the size of the dictionary can be limited so it fits into cache, decompression is made to be CPU bound rather than memory bound. To achieve this, a value prefix coding scheme is presented, wherein value prefixes are stored in the dictionary to get good compression from small dictionaries. Also presented is an algorithm that determines the optimal entries for a value prefix dictionary. Once the dictionary fits in cache, decompression speed is often limited by the cost of mispredicted branches during Huffman code processing. A novel way is presented to quantize Huffman code lengths to allow code processing to be performed with few instructions, no branches, and very little extra memory. Also presented is an algorithm for code length quantization that produces the optimal assignment of Huffman codes and show that the adverse effect of quantization on the compression ratio is quite small.