Fixed-function Graphics Research Articles

A 3D graphics rendering pipeline implementation based on the openCL massively parallel processing

Recently, massively-parallel computing libraries and devices are much widely used, in addition to the traditional 3D graphics systems. In this paper, we present a full 3D fixed-function graphics pipeline, based on the OpenCL, which is one of the most widely used massively-parallel computing library. The full 3D graphics features including WebGL, Web3D and others can be implemented on the massively-parallel computations, without underlying 3D graphics hardware support. Many previous works focused on another massively-parallel system of CUDA, which has a drawback of limited availability. In contrast, we designed and implemented a new architecture with OpenCL, which is now available on various computing devices, including most CPUs, GPUs, and at least theoretically, special-purpose embedded FPGAs. Our work provides full 3D graphics features on OpenCL-capable systems, without dedicated 3D graphics hardware, to finally make 3D graphics features ubiquitous. Technically, we used a top-down approach in its rendering, from the whole screen to precise pixels. At each stage, we tuned our OpenCL implementations and also their global and local parameter spaces. We present the details of our design and also the final result of our implementation, and show its correctness and efficiency.

A Simplified Implementation of the Fixed-Function Graphics Pipeline: DRM Approach

A Simplified Implementation of the Fixed-Function Graphics Pipeline: DRM Approach

Accelerated bulk memory operations on heterogeneous multi-core systems

A traditional fixed-function graphics accelerator has evolved into a programmable general-purpose graphics processing unit over the past few years, the general-purpose computing on GPU (GPGPU). Recently, revolutionary measures have been taken along this direction: an integrated GPU, i.e., CPUs and GPUs are integrated into the same package or even into the same die. However, considering a system-on-chip, the GPU takes up considerable silicon resources, but when running non-graphical workloads or non-GPGPU applications it is likely that overall system performance will not be affected. This paper presents a novel approach to accelerate conventional operations that are normally performed on CPUs, which are bulk memory operations such as memcpy or memcmp, using an integrated GPU. Offloading bulk memory operations to the GPU has many benefits: (i) The throughput GPU outperforms the CPU in bulk memory operations; (ii) for on-die GPUs with unified cache between the GPU and the CPU, the CPU can utilize the GPU private cache to store the moved data and reduce the CPU cache bottleneck; (iii) additional lightweight hardware can also support asynchronous offloads; and (iv) unlike the prior art using a dedicated hardware copy engine (e.g., DMA), our approach utilizes as much GPU hardware resources as possible. The performance results based on our solution showed that offloaded bulk memory operations outperform CPU up to 4.3 times faster on micro-benchmarks while using fewer resources. Using eight real-world applications and a cycle-based full-system simulation environment, five of eight applications showed about 30% speedup and two applications showed about 20% speedup.

R-GPU

Over the last decade, Graphics Processing Unit (GPU) architectures have evolved from a fixed-function graphics pipeline to a programmable, energy-efficient compute accelerator for massively parallel applications. The compute power arises from the GPU’s Single Instruction/Multiple Threads architecture: concurrently running many threads and executing them as Single Instruction/Multiple Data--style vectors. However, compute power is still lost due to cycles spent on data movement and control instructions instead of data computations. Even more cycles are lost on pipeline stalls resulting from long latency (memory) operations. To improve not only performance but also energy efficiency, we introduce R-GPU: a reconfigurable GPU architecture with communicating cores. R-GPU is an addition to a GPU, which can still be used as such, but also has the ability to reorganize the cores of a GPU in a reconfigurable network. In R-GPU data movement and control is implicit in the configuration of the network. Each core executes a fixed instruction, reducing instruction decode count and increasing energy efficiency. On a number of benchmarks we show an average performance improvement of 2.1 × over the same GPU without modifications. We further make a conservative power estimation of R-GPU which shows that power consumption can be reduced by 6%, leading to an energy consumption reduction of 55%, while area only increases by a mere 4%.

COMPASS

A traditional fixed-function graphics accelerator has evolved into a programmable general-purpose graphics processing unit over the last few years. These powerful computing cores are mainly used for accelerating graphics applications or enabling low-cost scientific computing. To further reduce the cost and form factor, an emerging trend is to integrate GPU along with the memory controllers onto the same die with the processor cores. However, given such a system-on-chip, the GPU, while occupying a substantial part of the silicon, will sit idle and contribute nothing to the overall system performance when running non-graphics workloads or applications lack of data-level parallelism. In this paper, we propose COMPASS, a compute shader-assisted data prefetching scheme, to leverage the GPU resource for improving single-threaded performance on an integrated system. By harnessing the GPU shader cores with very lightweight architectural support, COMPASS can emulate the functionality of a hardware-based prefetcher using the idle GPU and successfully improve the memory performance of single-thread applications. Moreover, thanks to its flexibility and programmability, one can implement the best performing prefetch scheme to improve each specific application as demonstrated in this paper. With COMPASS, we envision that a future application vendor can provide a custom-designed COMPASS shader bundled with its software to be loaded at runtime to optimize the performance. Our simulation results show that COMPASS can improve the single-thread performance of memory-intensive applications by 68% on average.

COMPASS

A traditional fixed-function graphics accelerator has evolved into a programmable general-purpose graphics processing unit over the last few years. These powerful computing cores are mainly used for accelerating graphics applications or enabling low-cost scientific computing. To further reduce the cost and form factor, an emerging trend is to integrate GPU along with the memory controllers onto the same die with the processor cores. However, given such a system-on-chip, the GPU, while occupying a substantial part of the silicon, will sit idle and contribute nothing to the overall system performance when running non-graphics workloads or applications lack of data-level parallelism. In this paper, we propose COMPASS, a compute shader-assisted data prefetching scheme, to leverage the GPU resource for improving single-threaded performance on an integrated system. By harnessing the GPU shader cores with very lightweight architectural support, COMPASS can emulate the functionality of a hardware-based prefetcher using the idle GPU and successfully improve the memory performance of single-thread applications. Moreover, thanks to its flexibility and programmability, one can implement the best performing prefetch scheme to improve each specific application as demonstrated in this paper. With COMPASS, we envision that a future application vendor can provide a custom-designed COMPASS shader bundled with its software to be loaded at runtime to optimize the performance. Our simulation results show that COMPASS can improve the single-thread performance of memory-intensive applications by 68% on average.

Graphics shaders: theory and practice

Programmable graphics shaders, programs that can be downloaded to a graphics processor (GPU) to carry out operations outside the fixed-function pipeline of earlier standards, have become a key feature of computer graphics. This book is designed to open computer graphics shader programming to the student, whether in a traditional class or on their own. It will complement texts based on fixed-function graphics APIs, specifically OpenGL. It introduces shader programming in general, and specifically the GLSL shader language. It also introduces a flexible, easy-to-use tool, glman, that helps you develop, test, and tune shaders outside an application that would use them.