Hipacc is a domain-specific language for ease of programming image processing applications on hardware accelerators such as GPUs. It relieves the burden of manually porting algorithms to hardware for developers with the help of domain- and architecture-specific knowledge. One fundamental operation in image processing is reduction. Global reduction operators are the building blocks of many widely used algorithms, including image normalization, similarity estimation, etc. This paper presents an efficient approach to perform parallel reductions on GPUs with Hipacc. Our proposed approach benefits from the continuous effort of performance and programmability improvement by hardware vendors, for example, by utilizing the latest low-level primitives from Nvidia. Results show our approach achieves a speedup of up to 3.43 over an existing Hipacc implementation with traditional optimization methods, and a speedup of up to 9.02 over an implementation using the Thrust library from Nvidia.

Optimizing data-intensive applications such as image processing for GPU targets with complex memory hierarchies requires to explore the tradeoffs among locality, parallelism, and computation. Loop fusion as one of the classical optimization techniques has been proven effective to improve locality at the function level. Algorithms in image processing are increasing their complexities and generally consist of many kernels in a pipeline. The inter-kernel communications are intensive and exhibit another opportunity for locality improvement at the system level. The scope of this paper is an optimization technique called kernel fusion for data locality improvement. We present a formal description of the problem by defining an objective function for locality optimization. By transforming the fusion problem to a graph partitioning problem, we propose a solution based on the minimum cut technique to search fusible kernels recursively. In addition, we develop an analytic model to quantitatively estimate potential locality improvement by incorporating domain-specific knowledge and architecture details. The proposed technique is implemented in an image processing DSL and source-to-source compiler called Hipacc, and evaluated over six image processing applications on three Nvidia GPUs. A geometric mean speedup of up to 2.52 can be observed in our experiments.

Programming image processing algorithms on hardware accelerators such as graphics processing units (GPUs) often exhibits a trade-off between software portability and performance portability. Domain-specific languages (DSLs) have proven to be a promising remedy, which enable optimizations and generation of efficient code from a concise, high-level algorithm representation.

Field Programmable Gate Arrays (FPGAs) excel at the implementation of local operators in terms of throughput per energy since the off-chip communication can be reduced with an application-specific on-chip memory configuration. Furthermore, data-level parallelism can efficiently be exploited through socalled loop coarsening, which processes multiple horizontal pixels simultaneously. Moreover, existing solutions for proper border handling in hardware show considerable resource overheads. In this paper, we first propose novel architectures for image border handling and loop coarsening, which can significantly reduce area. Second, we present a systematic analysis of these architectures including the formulation of analytical models for their area usage. Based on these models, we provide an algorithm for suggesting the most efficient hardware architecture for a given specification. Finally, we evaluate several implementations of our proposed architectures obtained through Vivado High-Level Synthesis (HLS). The synthesis results show that the proposed coarsening architecture uses 32% less registers for a 5-by-5 convolution with a 64 coarsening factor compared to previous works, whereas the proposed border handling architectures facilitate a decrease in the Look-up Table (LUT) usage by 36 %.

Domain-Specific Languages (DSLs) provide a high-level and domain-specific abstraction to describe algorithms within a certain domain concisely. Since a DSL separates the algorithm description from the actual target implementation, it offers a high flexibility among heterogeneous hardware targets, such as CPUs and GPUs. With the recent uprise of promising High-Level Synthesis (HLS) tools, like Vivado HLS and Altera OpenCL, FPGAs are becoming another attractive target architecture. Particularly in the domain of image processing, applications often come with stringent requirements regarding performance, energy efficiency, and power, for which FPGA have been proven to be among the most suitable architectures. In this work, we present the Hipacc framework, a DSL and source-to-source compiler for image processing. We show that domain knowledge can be captured to generate tailored implementations for C-based HLS from a common high-level DSL description targeting FPGAs. Our approach includes FPGA-specific memory architectures for handling point and local operators, as well as several high-level transformations. We evaluate our approach by comparing the resulting hardware accelerators to GPU implementations, generated from exactly the same DSL source code.

The parallelization of programs and distributing their workloads to multiple threads can be a challenging task. In addition to multi-threading, harnessing vector units in CPUs proves highly desirable. However, employing vector units to speed up programs can be quite tedious. Either a program developer solely relies on the auto-vectorization capabilities of the compiler or he manually applies vector intrinsics, which is extremely error-prone, difficult to maintain, and not portable at all.

A large portion of image processing applications often come with stringent requirements regarding performance, energy efficiency, and power. FPGAs have proven to be among the most suitable architectures for algorithms that can be processed in a streaming pipeline. Yet, designing imaging systems for FPGAs remains a very time consuming task. High-Level Synthesis, which has significantly improved due to recent advancements, promises to overcome this obstacle. In particular, Altera OpenCL is a handy solution for employing an FPGA in a heterogeneous system as it covers all device communication. However, to obtain efficient hardware implementations, extreme code modifications, contradicting OpenCL's data-parallel programming paradigm, are necessary. In this work, we explore the programming methodology that yields significantly better hardware implementations for the Altera Offline Compiler. We furthermore designed a compiler back end for a domain-specific source-to-source compiler to leverage the algorithm description to a higher level and generate highly optimized OpenCL code. Moreover, we advanced the compiler to support arbitrary bit width operations, which are fundamental to hardware designs. We evaluate our approach by discussing the resulting implementations throughout an extensive application set and comparing them with example designs, provided by Altera. In addition, as we can derive multiple implementations for completely different target platforms from the same domain-specific language source code, we present a comparison of the achieved implementations in contrast to GPU implementations.

Domain-specific languages (DSLs) provide high-level and domain-specific abstractions that allow expressive and concise algorithm descriptions. Since the description in a DSL hides also the properties of the target hardware, DSLs are a promising path to target different parallel and heterogeneous hardware from the same algorithm description. In theory, the DSL description can capture all characteristics of the algorithm that are required to generate highly efficient parallel implementations. However, most frameworks do not make use of this knowledge and the performance cannot reach that of optimized library implementations. In this article, we present the HIPAccframework, a DSL and source-to-source compiler for image processing. We show that domain knowledge can be captured in the language and that this knowledge enables us to generate tailored implementations for a given target architecture. Back ends for CUDA, OpenCL, and Renderscript allow us to target discrete graphics processing units (GPUs) as well as mobile, embedded GPUs. Exploiting the captured domain knowledge, we can generate specialized algorithm variants that reach the maximal achievable performance due to the peak memory bandwidth. These implementations outperform state-of-the-art domain-specific languages and libraries significantly.

Current tools for High-Level Synthesis (HLS) excel at exploiting Instruction-Level Parallelism (ILP), the support for Data-Level Parallelism (DLP), one of the key advantages of Field Programmable Gate Arrays (FPGAs), is in contrast very limited. This work examines the exploitation of DLP on FPGAs using code generation for C-based HLS of image filters and streaming pipelines, consisting of point and local operators. In addition to well known loop tiling techniques, we propose loop coarsening, which delivers superior performance and scalability. Loop tiling corresponds to splitting an image into separate regions, which are then processed in parallel by replicated accelerators. For data streaming, this also requires the generation of glue logic for the distribution of image data. Conversely, loop coarsening allows to process multiple pixels in parallel, whereby only the kernel operator is replicated within a single accelerator. We augment the FPGA back end of the heterogeneous Domain-Specific Language (DSL) framework HIPAcc by loop coarsening and compare the resulting FPGA accelerators to highly optimized software implementations for Graphics Processing Units (GPUs), all generated from the exact same code base. Moreover, we demonstrate the advantages of code generation for algorithm development by outlining how design space exploration enabled by HIPAcccan yield a more efficient implementation than hand-coded VHDL.

The success of Android is based on its unified Java programming model that allows to write platform-independent programs for a variety of different target platforms. However, this comes at the cost of performance. As a consequence, Google introduced APIs that allow to write native applications and to exploit multiple cores as well as embedded GPUs for compute-intensive parts. This paper proposes code generation techniques in order to target the Renderscript and Filterscript APIs. Renderscript harnesses multi-core CPUs and unified shader GPUs, while the more restricted Filterscript also supports GPUs with earlier shader models. Our techniques focus on image processing applications and allow to target these APIs and OpenCL from a common description. We further supersede memory transfers by sharing the same memory region among different processing elements on HSA platforms. As reference, we use an embedded platform hosting a multi-core ARM CPU and an ARM Mali GPU. We show that our generated source code is faster than native implementations in OpenCV as well as the pre-implemented script intrinsics provided by Google for acceleration on the embedded GPU.

We introduce a novel class of massively parallel processor architectures called invasive Tightly-Coupled Processor Arrays (TCPAs). The presented processor class is a highly parameterizable template which can be tailored before runtime to fulfill costumers' requirements such as performance, area cost, and energy efficiency. These programmable accelerators are well suited for domain-specific computing from the areas of signal, image, and video processing as well as other streaming processing applications. To overcome future scaling issues (e.g., power consumption, reliability, resource management, as well as application parallelization and mapping), TCPAs are inherently designed in way that they support self-adaptivity and resource awareness at hardware level. Here, we follow a recently introduced resource-aware parallel computing paradigm called invasive computing where an application can dynamically claim, execute, and release the resources. Furthermore, we show how invasive computing can be used as an enabler for power management. For the first time, we present a seamless mapping flow for TCPAs, based on a domain-specific language. Moreover, we outline a complete symbolic mapping approach. Finally, we support our claims by comparing a TCPA against an ARM Mali-T604 GPU in terms of performance and energy efficiency.