Architecture of Computing Systems (Lecture Notes in Computer ...

Compiler-Directed Performance Model Construction 189
The approach is based on the manual instrumentation of the application with
MA API calls. The models for floating point and load/store operations are created
at run-time of the application. The used MA library starts and stops the
performance counters (using PAPI), which are used to validate the model. The
MPI communication patterns are profiled. Further, an incremental refinement of
the model is supported through adding more variables to the input parameters.
The MA library functions generate trace files. These traces are used to validate
the model. The CG and SP benchmarks of the NPB-suite were evaluated and
reveal a maximum error rate less than 30% (typical error rate less than 10%) for
floating point operations.
Ipek et. al. propose a neural network based approach towards performance
prediction of parallel applications [6]. Through training neural networks on performance
data, this approach benefits from automated model construction, modeling
full system complexity without the need to add architectural details to the
model to get precise predictions.
Marin describes an architecture independent model construction process [7].
The object code of an application is analyzed in two ways. First, a static analysis
is performed, identifying the loop nests, instruction mixes in basic blocks
and delivering a control flow graph. Second, the binary is instrumented to measure
the basic block counts, the communication volume and frequency and the
memory utilization at runtime. The post-processing tool set generates an architecture
neutral model, which serves as input for the scheduler and is merged
with an architecture description leading to an overall performance model. The
scheduler maps the specific instructions to generic classes, assuring the architecture
independence. The resulting models are capable of predicting floating
point, load and store operations. This approach is extended for cross-platform
prediction [8]. The effects of restricting the execution time on cross-platform
performance prediction are studied by Yang et. al [9].
The convolution method was developed at the Performance Modeling and
Characterization Lab at the San Diego Supercomputer Center [10]. The performance
prediction of a parallel application is obtained from single processor
performance and network utilization.
Lee et al. propose to apply statistical methods to combine single processor
and contention models to predict multiprocessor behavior [11,12].
The Performance Oriented End-to-end Modeling System (POEMS) is an environment
for end-to-end performance modeling of parallel systems [13]. System
components are modeled on different layers of abstraction: applications, runtime,
operating system, and hardware. Different modeling paradigms, such as simulation,
analysis, and direct measurement, are employed to model the components.
Sweep3D is used to evaluate and predict parallel architectures.
TAU is a set of tools addressing instrumentation, measurement and analysis
of parallel applications [14]. However, their approach does not involve a compiler
but instead relies on the combination of a preprocessor and binary instrumentation
or dynamic instrumentation to realize an instrumented binary. Binary instrumentation
has the advantage to work even in the absence of the source code.