Information Categories of the Benchmark Characterization Methodology and Objectives.

Information Category	Questions to be Answered and the Target Audience
Application description	A basic understanding of the problem being solved by a benchmark is important for all user classes. For many users it is important to understand how representative of a specific discipline a benchmark is. The latter also helps in selecting the benchmarks and in evaluating the quality of the results.
Program description	Describes programming languages, size, system requirements (cpu, memory, I/O), number of subroutines, and software libraries. The problem is described from a software engineering perspective.
Application component structure	Describes the coarse-grain structure of the application and opportunities for execution across parallel and/or heterogeneous, (globally) distributed systems.
Algorithmic structure	Algorithmic decomposition is important for the general understanding, but specifically aimed at algorithm research. This category includes measured improvements due to algorithmic changes.
Measured performance	This addresses basic performance questions of all audiences. It indicated basic scalability, suitability of a code for specific machines, effectiveness of exploiting parallel processors, and sensitivity to changes in input parameters. It also states the baseline for improvements of algorithms, compilers, and architectures.
Analysis with respect to programming models	SPEChpc benchmarks include a message passing parallel and a shared-memory (directive-parallel) code variant. Message-passing profiles answer questions about: suitability of a distributed-memory architecture, its interconnections, and message libraries, scalability beyond the measured range, opportunities for communication optimizations, and heterogeneity of the application and coupling of the components.  Shared-memory analyses indicate: sources of performance loss, data-sharing patterns, and opportunities for shared data optimizations.
I/O characterization	Leads to a basic understanding of the application's I/O component. Shows scalability with respect to I/O, and sensitivity of the application with respect to the machine's I/O subsystems, (e.g., parallel disk organizations and partitioning schemes.)
Cache analysis	Shows the application's locality properties. Gives detailed insight into the data sharing behavior of the shared-memory application version and the data reuse of individual nodes in the message-passing version. The analysis follows the comprehensive cache characterization model introduced in Technical report, HPCLAB, 98: A methodology and a tool for cache characterization of loop-parallel programs.
Instruction analysis	Shows the number and type of instructions executed and the instruction profile (e.g., loads vs. stores.) Indicates detailed timing information, (e.g., pipeline stalls.) This information is the basis for the detailed performance analysis of individual code sections.
Program analysis	This is a large category of primary interest to compiler researchers and a basis for manual code improvements. It includes call-graph information, data use and access patterns, the control-flow structure, and results of compiler analyses, (e.g., applied and failed optimizations and program statistics.)
Simulation Analysis	This is an open-ended category. Simulations can give insight into almost all parameters of an application and its potential execution behavior on a new or idealized machine.
Advanced model analysis	Various advanced performance analysis and prediction models have been proposed in the literature. They can give insight into a code's complex behavior, sources of performance loss, and upper performance limits.