Although unstructured mesh algorithms are a popular means of solving problems across a broad range of disciplines---from texture mapping to computational fluid dynamics---they are often dominated not by computation, but by mesh overhead. Our study of an object-oriented mesh-based benchmark reveals that 72% of its execution time is spent on mesh-related operations, such as iterating over faces or chasing pointers. We report a series of optimizations---some traditional, some novel---that dramatically improve the benchmark's computational intensity---the ratio of floating point operations to memory accesses. This improvement is attributable to an eight-fold reduction in memory operations and results in a 4.7x speedup in execution time.Our work demonstrates that common subexpression elimination and code motion are important optimizations for mesh-based codes. However, conservative analysis prevents their application. We discuss these barriers to analysis and argue that an understanding of mesh semantics complements more traditional analyses, such as pointer alias analysis, and certifies the correctness of these optimizations. Our identification of overheads in mesh-based codes, optimizations that address them, and limitations of current compiler analyses are required for our eventual goal of automating these optimizations in a semantics-aware compiler.

PerfTrack is a data store and interface for managing performance data from large-scale parallel applications. Data collected in different locations and formats can be compared and viewed in a single performance analysis session. The underlying data store used in PerfTrack is implemented with a database management system (DBMS). PerfTrack includes interfaces to the data store and scripts for automatically collecting data describing each experiment, such as build and platform details. We have implemented a prototype of PerfTrack that can use Oracle or PostgreSQL for the data store. We demonstrate the prototype's functionality with three case studies: one is a comparative study of an ASC purple benchmark on high-end Linux and AIX platforms; the second is a parameter study conducted at Lawrence Livermore National Laboratory (LLNL) on two high end platforms, a 128 node cluster of IBM Power 4 processors and BlueGene/L; the third demonstrates incorporating performance data from the Paradyn Parallel Performance Tool into an existing PerfTrack data store.

Abstract Sophisticated parallel languages are difficult to develop; most parallel distributed memory scientific applications are developed using a serial language, expressing parallelism through third party libraries (e.g. MPI). As a result, frameworks and libraries are often used to encapsulate significant complexities. We define a novel approach to optimize the use of libraries within applications. The resulting tool, named ROSE, leverages the additional semantics provided by library‐defined abstractions enabling library specific optimization of application codes. It is a common perception that performance is inversely proportional to the level of abstraction. Our work shows that this is not the case if the additional semantics can be leveraged. We show how ROSE can be used to leverage the semantics within the compile‐time optimization. Copyright © 2004 John Wiley & Sons, Ltd.