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:
Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I O model such as the one implemented in PostgreSQL may make query response time predictions that are up to 2× slower than the optimal multi-join query plan over memory-resident data. This paper introduces a memory I O cost model to identify good evaluation strategies for complex query plans with multiple hash-based equi-joins over memory-resident data. The proposed cost model is carefully validated for accuracy using three different systems, including an Amazon EC2 instance, to control for hardware-specific differences. Prior work in parallel query evaluation has advocated right-deep and bushy trees for multi-join queries due to their greater parallelization and pipelining potential. A surprising finding is that the conventional wisdom from shared-nothing disk-based systems does not directly apply to the modern shared-everything memory hierarchy. As corroborated by our model, the performance gap between the optimal left-deep and right-deep query plan can grow to about 10× as the number of joins in the query increases.

Cited Abstract(s):
[1]: textabstractIn the past decade, advances in speed of commodity CPUs have far out-paced advances in memory latency. Main-memory access is therefore increasingly a performance bottleneck for many computer applications, including database systems. In this article, we use a simple scan test to show the severe impact of this bottleneck. The insights gained are translated into guidelines for database architecture; in terms of both data structures and algorithms. We discuss how vertically fragmented data structures optimize cache performance on sequential data access. We then focus on equi-join, typically a random-access operation, and introduce radix algorithms for partitioned hash-join. The performance of these algorithms is quantified using a detailed analytical model that incorporates memory access cost. Experiments that validate this model were performed on the Monet database system. We obtained exact statistics on events like TLB misses, L1 and L2 cache misses, by using hardware performance counters found in modern CPUs. Using our cost model, we show how the carefully tuned memory access pattern of our radix algorithms make them perform well, which is confirmed by experimental results.

[2]: Recent high-performance processors employ sophisticated techniques to overlap and simultaneously execute multiple computation and memory operations. Intuitively, these techniques should help database applications, which are becoming increasingly compute and memory bound. Unfortunately, recent studies report that faster processors do not improve database system performance to the same extent as scientific workloads. Recent work on database systems focusing on minimizing memory latencies, such as cache-conscious algorithms for sorting and data placement, is one step toward addressing this problem. However, to best design high performance DBMSs we must carefully evaluate and understand the processor and memory behavior of commercial DBMSs on today’s hardware platforms. In this paper we answer the question “Where does time go when a database system is executed on a modern computer platform?” We examine four commercial DBMSs running on an Intel Xeon and NT 4.0. We introduce a framework for analyzing query execution time on a DBMS running on a server with a modern processor and memory architecture. To focus on processor and memory interactions and exclude effects from the I O subsystem, we use a memory resident database. Using simple queries we find that database developers should (a) optimize data placement for the second level of data cache, and not the first, (b) optimize instruction placement to reduce first-level instruction cache stalls, but (c) not expect the overall execution time to decrease significantly without addressing stalls related to subtle implementation issues (e.g., branch prediction).

[3]: With the availability of very large, relatively inexpensive main memories, it is becoming possible keep large databases resident in main memory In this paper we consider the changes necessary to permit a relational database system to take advantage of large amounts of main memory We evaluate AVL vs B+-tree access methods for main memory databases, hash-based query processing strategies vs sort-merge, and study recovery issues when most or all of the database fits in main memory As expected, B+-trees are the preferred storage mechanism unless more than 80--90 of the database fits in main memory A somewhat surprising result is that hash based query processing strategies are advantageous for large memory situations

[4]: Most previous work in the area of main memory database systems has focused on the problem of developing query processing techniques that work well with a very large buffer pool. In this paper, we address query processing issues for memory resident relational databases, an environment with a very different set of costs and priorities. We present an architecture for a main memory DBMS, discussing the ways in which a memory resident database differs from a disk-based database. We then address the problem of processing relational queries in this architecture, considering alternative algorithms for selection, projection, and join operations and studying their performance. We show that a new index structure, the T Tree, works well for selection and join processing in memory resident databases. We also show that hashing methods work well for processing projections and joins, and that an old join method, sort-merge, still has a place in main memory.

[5]: The current main memory (DRAM) access speeds lag far behind CPU speeds. Cache memory, made of static RAM, is being used in today’s architectures to bridge this gap. It provides access latencies of 2-4 processor cycles, in contrast to main memory which requires 15-25 cycles. Therefore, the performance of the CPU depends upon how well the cache can be utilized. We show that there are significant benefits in redesigning our traditional query processing algorithms so that they can make better use of the cache. The new algorithms run 8 -200 faster than the traditional ones.
* Query processing techniques for in-memory data Research in main memory database management systems commenced more than three decades ago. Early work investigated join algorithms for main memory database systems by [3] and an investigation of in-memory query processing techniques by Lehman and Carey [4] . As the CPU architecture evolved, additional hardware operations were shown to become performance bottlenecks. [2] studied the performance bottlenecks of four commercial database management systems and highlighted the importance of minimizing L2 data cache stalls and L1 instruction cache stalls. [5] proposed to make the join algorithm cache-conscious to improve locality and join performance by adding a partitioning step before the join. [1] proposed radix-based query processing techniques to improve in-memory performance by reducing cache and TLB misses.