OpenDwarfs

"The OpenDwarfs project provides a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore and GPU systems. The first instantiation of the OpenDwarfs has been realized in OpenCL.

The proliferation of heterogeneous computing platforms presents the parallel computing community with new challenges. One such challenge entails evaluating the efficacy of such parallel architectures and identifying the architectural innovations that ultimately benefit applications. To address this challenge, we need benchmarks that capture the execution patterns (i.e., dwarfs or motifs) of applications, both present and future, in order to guide future hardware design. Furthermore, we desire a common programming model for the benchmarks that facilitates code portability across a wide variety of different processors (e.g., CPU, APU, GPU, FPGA, DSP) and computing environments (e.g., embedded, mobile, desktop, server). As such, we present the latest release of OpenDwarfs, a benchmark suite that currently realizes the Berkeley dwarfs in OpenCL, a vendor-agnostic and open-standard computing language for parallel computing. Using OpenDwarfs, we characterize a diverse set of modern fixed and reconfigurable parallel platforms: multi-core CPUs, discrete and integrated GPUs, Intel Xeon Phi co-processor, as well as a FPGA. We describe the computation and communication patterns exposed by a representative set of dwarfs, obtain relevant profiling data and execution information, and draw conclusions that highlight the complex interplay between dwarfs’ patterns and the underlying hardware architecture of modern parallel platforms."

https://github.com/vtsynergy/OpenDwarfs

http://view.eecs.berkeley.edu/wiki/Dwarf_Mine

http://www.cs.virginia.edu/~skadron/wiki/rodinia/index.php/Rodinia:Accelerating_Compute-Intensive_Applications_with_Accelerators

OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures - http://hgpu.org/?p=15143

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing - https://hgpu.org/?p=17224

Our proposed approach is based on OpenCL implementations of the Berkeley dwarfs. We use our benchmark suite (OpenDwarfs) in characterizing performance of state-of-the-art parallel architectures, and as the main component of a methodology (Telescoping Architectures) for identifying trends in future heterogeneous architectures. Furthermore, we employ OpenDwarfs in a multi-faceted study on the gaps between the three P’s in the context of the modern heterogeneous computing landscape. Our case-study spans a variety of compilers, languages, optimizations, and target architectures, including the CPU, GPU, MIC, and FPGA. Based on our insights, and extending aspects of prior research (e.g., in compilers, programming languages, and auto-tuning), we propose the introduction of grid-based data structures as the basis of programming frameworks and present a prototype unified framework (GLAF) that encompasses a novel visual programming environment with code generation, auto-parallelization, and auto-tuning capabilities. Our results, which span scientific domains, indicate that our holistic approach constitutes a viable alternative towards enhancing the three P’s and further democratizing heterogeneous, parallel computing for non-programming-savvy audiences, and especially domain scientists.