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Arrays of integers are often compressed in search engines. Though there are many ways to compress integers, we are interested in the popular byte-oriented integer compression techniques (e.g., VByte or Googles Varint-GB). They are appealing due to their simplicity and engineering convenience. Amazons varint-G8IU is one of the fastest byte-oriented compression technique published so far. It makes judicious use of the powerful single-instruction-multiple-data (SIMD) instructions available in commodity processors. To surpass varint-G8IU, we present Stream VByte, a novel byte-oriented compression technique that separates the control stream from the encoded data. Like varint-G8IU, Stream VByte is well suited for SIMD instructions. We show that Stream VByte decoding can be up to twice as fast as varint-G8IU decoding over real data sets. In this sense, Stream VByte establishes new speed records for byte-oriented integer compression, at times exceeding the speed of the memcpy function. On a 3.4GHz Haswell processor, it decodes more than 4 billion differentially-coded integers per second from RAM to L1 cache.
We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation
Sorted lists of integers are commonly used in inverted indexes and database systems. They are often compressed in memory. We can use the SIMD instructions available in common processors to boost the speed of integer compression schemes. Our S4-BP128-
Deep learning for recommendation data is the one of the most pervasive and challenging AI workload in recent times. State-of-the-art recommendation models are one of the largest models rivalling the likes of GPT-3 and Switch Transformer. Challenges i
Motivated by the prevalent data science applications of processing and mining large-scale graph data such as social networks, web graphs, and biological networks, as well as the high I/O and communication costs of storing and transmitting such data,
As a highly data-driven application, recommender systems could be affected by data bias, resulting in unfair results for different data groups, which could be a reason that affects the system performance. Therefore, it is important to identify and so