1,270 research outputs found
Efficient Hardware Design Of Iterative Stencil Loops
A large number of algorithms for multidimensional signals processing and scientific computation come in the form of iterative stencil loops (ISLs), whose data dependencies span across multiple iterations. Because of their complex inner structure, automatic hardware acceleration of such algorithms is traditionally considered as a difficult task.
In this paper, we introduce an automatic design flow that identifies, in a wide family of bidimensional data processing algorithms, sub-portions that exhibit a kind of parallelism close to that of ISLs; these are mapped onto a space of highly optimized ad-hoc architectures, which is efficiently explored to identify the best implementations with respect to both area and throughput. Experimental results show that the proposed methodology generates circuits whose performance is comparable to that of manually-optimized solutions, and orders of magnitude higher than those generated by commercial
HLS tools
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
Efficient multicore-aware parallelization strategies for iterative stencil computations
Stencil computations consume a major part of runtime in many scientific
simulation codes. As prototypes for this class of algorithms we consider the
iterative Jacobi and Gauss-Seidel smoothers and aim at highly efficient
parallel implementations for cache-based multicore architectures. Temporal
cache blocking is a known advanced optimization technique, which can reduce the
pressure on the memory bus significantly. We apply and refine this optimization
for a recently presented temporal blocking strategy designed to explicitly
utilize multicore characteristics. Especially for the case of Gauss-Seidel
smoothers we show that simultaneous multi-threading (SMT) can yield substantial
performance improvements for our optimized algorithm.Comment: 15 pages, 10 figure
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