You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms.

Cited Abstract(s):
[1]: In the past decades, advances in speed of commodity CPUs have far outpaced advances in RAM latency. Main-memory access has therefore become a performance bottleneck for many computer applications; a phenomenon that is widely known as the "memory wall." In this paper, we report how research around the MonetDB database system has led to a redesign of database architecture in order to take advantage of modern hardware, and in particular to avoid hitting the memory wall. This encompasses (i) a redesign of the query execution model to better exploit pipelined CPU architectures and CPU instruction caches; (ii) the use of columnar rather than row-wise data storage to better exploit CPU data caches; (iii) the design of new cache-conscious query processing algorithms; and (iv) the design and automatic calibration of memory cost models to choose and tune these cache-conscious algorithms in the query optimizer.
A less-discussed, but just as important trend over the past two decades is the so-called memory wall'' [1] , where increases in processor speed have far outpaced improvements in memory latency. This means that RAM is becoming slower relative to the CPU. In the 1980s, memory latencies were on the order of a few clock cycles; today, it could be several hundred clock cycles. To hide this latency, computer architects have introduced hierarchical cache memories: a typical server today will have L1, L2, and L3 caches between the processor and main memory. Cache architectures are built on the assumption of reference locality---that at any given time, the processor repeatedly accesses only a (relatively) small amount of data, and these fit into cache. The fraction of memory accesses that can be fulfilled directly from the cache is called the cache hit rate , and data not found in cache is said to cause a cache miss . Cache misses cascade down the hierarchy---if a datum is not found in L1, the processor tries to look for it in L2, then in L3, and finally in main memory (paying an increasing latency cost each level down).