5,182 research outputs found
ScALPEL: A Scalable Adaptive Lightweight Performance Evaluation Library for application performance monitoring
As supercomputers continue to grow in scale and capabilities, it is becoming
increasingly difficult to isolate processor and system level causes of
performance degradation. Over the last several years, a significant number of
performance analysis and monitoring tools have been built/proposed. However,
these tools suffer from several important shortcomings, particularly in
distributed environments. In this paper we present ScALPEL, a Scalable Adaptive
Lightweight Performance Evaluation Library for application performance
monitoring at the functional level. Our approach provides several distinct
advantages. First, ScALPEL is portable across a wide variety of architectures,
and its ability to selectively monitor functions presents low run-time
overhead, enabling its use for large-scale production applications. Second, it
is run-time configurable, enabling both dynamic selection of functions to
profile as well as events of interest on a per function basis. Third, our
approach is transparent in that it requires no source code modifications.
Finally, ScALPEL is implemented as a pluggable unit by reusing existing
performance monitoring frameworks such as Perfmon and PAPI and extending them
to support both sequential and MPI applications.Comment: 10 pages, 4 figures, 2 table
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
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