Master Thesis - ICS

Computer Science Department
Antonis Misargopoulos
task A, computation for task B, and communication between the processors on which they
reside are coordinated. Such an approach is taken in [82] which describes a compositional
model for predicting lag-time in interactive virtual reality simulations.
3.3 Scheduling via Dynamic Programming
Extensive research has been carried out on the design, implementation and performance
of parallel database systems. Teradata [94], Budda [95], HC186-16 [96], GAMMA [97],
and XPRS [98] are examples of systems that were actually implemented, and many
papers were written on their performance. The performance evaluation of these systems
though is mainly limited to simple queries that involve no more than one or two join
operations. Recent developments in the direction of nonstandard applications, the use of
complex data models, and the availability of high-level interfaces tend to generate
complex queries that may contain larger numbers of joins between relations.
Consequently, the development of execution strategies for the parallel evaluation of
multi-join queries has drawn the attention of the scientific community.
A number of strategies has been proposed [99, 100, 101, 102, 103] and their
performance was evaluated via simulation. However, no comparative experimental
performance evaluation is available. In [93] there is a comparative performance
evaluation of four execution strategies by implementing all of them on the same parallel
database system, PRISMA/DB. Experiments have been done up to 80 processors. The
basic strategy is to first determine an execution schedule with minimum total cost and
then parallelize this schedule with one of the four execution strategies. These strategies,
coming from the literature, are named: Sequential Parallel, Synchronous Execution,
Segmented Right-Deep, and Full Parallel. Based on the experiments clear guidelines are
given when to use which strategy.
One central component of a query optimizer is its search strategy or enumeration
algorithm. The enumeration algorithm of the optimizer determines which plans to
enumerate, and the classic enumeration algorithm is based on Dynamic Programming.
This algorithm was proposed in IBM’s System R project [145], and it is used in most
query optimizers today. System R [145] is the pioneer in the area of optimization of
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