293 research outputs found
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Optimizing transformations of stencil operations for parallel object-oriented scientific frameworks on cache-based architectures
High-performance scientific computing relies increasingly on high-level large-scale object-oriented software frameworks to manage both algorithmic complexity and the complexities of parallelism: distributed data management, process management, inter-process communication, and load balancing. This encapsulation of data management, together with the prescribed semantics of a typical fundamental component of such object-oriented frameworks--a parallel or serial array-class library--provides an opportunity for increasingly sophisticated compile-time optimization techniques. This paper describes two optimizing transformations suitable for certain classes of numerical algorithms, one for reducing the cost of inter-processor communication, and one for improving cache utilization; demonstrates and analyzes the resulting performance gains; and indicates how these transformations are being automated
From Physics Model to Results: An Optimizing Framework for Cross-Architecture Code Generation
Starting from a high-level problem description in terms of partial
differential equations using abstract tensor notation, the Chemora framework
discretizes, optimizes, and generates complete high performance codes for a
wide range of compute architectures. Chemora extends the capabilities of
Cactus, facilitating the usage of large-scale CPU/GPU systems in an efficient
manner for complex applications, without low-level code tuning. Chemora
achieves parallelism through MPI and multi-threading, combining OpenMP and
CUDA. Optimizations include high-level code transformations, efficient loop
traversal strategies, dynamically selected data and instruction cache usage
strategies, and JIT compilation of GPU code tailored to the problem
characteristics. The discretization is based on higher-order finite differences
on multi-block domains. Chemora's capabilities are demonstrated by simulations
of black hole collisions. This problem provides an acid test of the framework,
as the Einstein equations contain hundreds of variables and thousands of terms.Comment: 18 pages, 4 figures, accepted for publication in Scientific
Programmin
Devito: Towards a generic Finite Difference DSL using Symbolic Python
Domain specific languages (DSL) have been used in a variety of fields to
express complex scientific problems in a concise manner and provide automated
performance optimization for a range of computational architectures. As such
DSLs provide a powerful mechanism to speed up scientific Python computation
that goes beyond traditional vectorization and pre-compilation approaches,
while allowing domain scientists to build applications within the comforts of
the Python software ecosystem. In this paper we present Devito, a new finite
difference DSL that provides optimized stencil computation from high-level
problem specifications based on symbolic Python expressions. We demonstrate
Devito's symbolic API and performance advantages over traditional Python
acceleration methods before highlighting its use in the scientific context of
seismic inversion problems.Comment: pyHPC 2016 conference submissio
Transformations of High-Level Synthesis Codes for High-Performance Computing
Specialized hardware architectures promise a major step in performance and
energy efficiency over the traditional load/store devices currently employed in
large scale computing systems. The adoption of high-level synthesis (HLS) from
languages such as C/C++ and OpenCL has greatly increased programmer
productivity when designing for such platforms. While this has enabled a wider
audience to target specialized hardware, the optimization principles known from
traditional software design are no longer sufficient to implement
high-performance codes. Fast and efficient codes for reconfigurable platforms
are thus still challenging to design. To alleviate this, we present a set of
optimizing transformations for HLS, targeting scalable and efficient
architectures for high-performance computing (HPC) applications. Our work
provides a toolbox for developers, where we systematically identify classes of
transformations, the characteristics of their effect on the HLS code and the
resulting hardware (e.g., increases data reuse or resource consumption), and
the objectives that each transformation can target (e.g., resolve interface
contention, or increase parallelism). We show how these can be used to
efficiently exploit pipelining, on-chip distributed fast memory, and on-chip
streaming dataflow, allowing for massively parallel architectures. To quantify
the effect of our transformations, we use them to optimize a set of
throughput-oriented FPGA kernels, demonstrating that our enhancements are
sufficient to scale up parallelism within the hardware constraints. With the
transformations covered, we hope to establish a common framework for
performance engineers, compiler developers, and hardware developers, to tap
into the performance potential offered by specialized hardware architectures
using HLS
AUTOMATING DATA-LAYOUT DECISIONS IN DOMAIN-SPECIFIC LANGUAGES
A long-standing challenge in High-Performance Computing (HPC) is the simultaneous achievement of programmer productivity and hardware computational efficiency. The challenge has been exacerbated by the onset of multi- and many-core CPUs and accelerators. Only a few expert programmers have been able to hand-code domain-specific data transformations and vectorization schemes needed to extract the best possible performance on such architectures. In this research, we examined the possibility of automating these methods by developing a Domain-Specific Language (DSL) framework. Our DSL approach extends C++14 by embedding into it a high-level data-parallel array language, and by using a domain-specific compiler to compile to hybrid-parallel code. We also implemented an array index-space transformation algebra within this high-level array language to manipulate array data-layouts and data-distributions. The compiler introduces a novel method for SIMD auto-vectorization based on array data-layouts. Our new auto-vectorization technique is shown to outperform the default auto-vectorization strategy by up to 40% for stencil computations. The compiler also automates distributed data movement with overlapping of local compute with remote data movement using polyhedral integer set analysis. Along with these main innovations, we developed a new technique using C++ template metaprogramming for developing embedded DSLs using C++. We also proposed a domain-specific compiler intermediate representation that simplifies data flow analysis of abstract DSL constructs. We evaluated our framework by constructing a DSL for the HPC grand-challenge domain of lattice quantum chromodynamics. Our DSL yielded performance gains of up to twice the flop rate over existing production C code for selected kernels. This gain in performance was obtained while using less than one-tenth the lines of code. The performance of this DSL was also competitive with the best hand-optimized and hand-vectorized code, and is an order of magnitude better than existing production DSLs.Doctor of Philosoph
Polyhedral+Dataflow Graphs
This research presents an intermediate compiler representation that is designed for optimization, and emphasizes the temporary storage requirements and execution schedule of a given computation to guide optimization decisions. The representation is expressed as a dataflow graph that describes computational statements and data mappings within the polyhedral compilation model. The targeted applications include both the regular and irregular scientific domains.
The intermediate representation can be integrated into existing compiler infrastructures. A specification language implemented as a domain specific language in C++ describes the graph components and the transformations that can be applied. The visual representation allows users to reason about optimizations. Graph variants can be translated into source code or other representation. The language, intermediate representation, and associated transformations have been applied to improve the performance of differential equation solvers, or sparse matrix operations, tensor decomposition, and structured multigrid methods
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Improving scalability with loop transformations and message aggregation in parallel object-oriented frameworks for scientific computing
Application codes reliably achieve performance far less than the advertised capabilities of existing architectures, and this problem is worsening with increasingly-parallel machines. For large-scale numerical applications, stencil operations often impose the great part of the computational cost, and the primary sources of inefficiency are the costs of message passing and poor cache utilization. This paper proposes and demonstrates optimizations for stencil and stencil-like computations for both serial and parallel environments that ameliorate these sources of inefficiency. Achieving scalability, they believe, requires both algorithm design and compile-time support. The optimizations they present are automatable because the stencil-like computations are implemented at a high level of abstraction using object-oriented parallel array class libraries. These optimizations, which are beyond the capabilities of today compilers, may be performed automatically by a preprocessor such as the one they are currently developing
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