Abstract-Programming accelerators such as GPUs with low-level APIs and languages such as OpenCL and CUDA is difficult, error-prone, and not performance-portable. Automatic parallelization and domain specific languages (DSLs) have been proposed to hide complexity and regain performance portability. We present PENCIL, a rigorously-defined subset of GNU C99-enriched with additional language constructs-that enables compilers to exploit parallelism and produce highly optimized code when targeting accelerators. PENCIL aims to serve both as a portable implementation language for libraries, and as a target language for DSL compilers.We implemented a PENCIL-to-OpenCL backend using a state-of-the-art polyhedral compiler. The polyhedral compiler, extended to handle data-dependent control flow and non-affine array accesses, generates optimized OpenCL code. To demonstrate the potential and performance portability of PENCIL and the PENCIL-to-OpenCL compiler, we consider a number of image processing kernels, a set of benchmarks from the Rodinia and SHOC suites, and DSL embedding scenarios for linear algebra (BLAS) and signal processing radar applications (SpearDE), and present experimental results for four GPU platforms: AMD Radeon HD 5670 and R9 285, NVIDIA GTX 470, and ARM Mali-T604.

The efficiency of tensor contraction is of great importance. Compilers cannot optimize it well enough to come close to the performance of expert-tuned implementations. All existing approaches that provide competitive performance require optimized external code. We introduce a compiler optimization that reaches the performance of optimized BLAS libraries without the need for an external implementation or automatic tuning. Our approach provides competitive performance across hardware architectures and can be generalized to deliver the same benefits for algebraic path problems. By making fast linear algebra kernels available to everyone, we expect productivity increases when optimized libraries are not available. CCS Concepts: • Software and its engineering → Compilers; • Computing methodologies → Linear algebra algorithms;

The search for approaches to a holistic sustainable agriculture requires the development of new cropping systems that provide additional ecosystem services beyond biomass supply for food, feed, material, and energy use. The reduction of chemical synthetic plant protection products is a key instrument to protect vulnerable natural resources such as groundwater and biodiversity. Together with an optimal use of mineral fertilizer, agroecological practices, and precision agriculture technologies, a complete elimination of chemical synthetic plant protection in mineral-ecological cropping systems (MECSs) may not only improve the environmental performance of agroecosystems, but also ensure their yield performance. Therefore, the development of MECSs aims to improve the overall ecosystem services of agricultural landscapes by (i) improving the provision of regulating ecosystem services compared to conventional cropping systems and (ii) improving the supply of provisioning ecosystem services compared to organic cropping systems. In the present review, all relevant research levels and aspects of this new farming concept are outlined and discussed based on a comprehensive literature review and the ongoing research project “Agriculture 4.0 without Chemical-Synthetic Plant Protection”.

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