SUMMARY H.264/MPEG‐4 part 10 is the latest standard for video compression and promises a significant advance in terms of quality and distortion compared with the commercial standards currently most in use such as MPEG‐2 or MPEG‐4. To achieve this better performance, H.264 adopts a large number of new/improved compression techniques compared with previous standards, albeit at the expense of higher computational complexity. In addition, in recent years new hardware accelerators have emerged, such as graphics processing units (GPUs), which provide a new opportunity to reduce complexity for a large variety of algorithms. However, current GPUs suffer from higher power consumption requirements because of its design. Up to now, GPU‐based software developers have not taken this into account. In this paper, we present a detailed procedure to implement the H.264 motion estimation for a GPU, with the aim of reducing time and, as a consequence, the energy consumption. The results show a negligible drop in rate distortion with a time reduction of over 91.5% on average and it reduces the energy consumption by a factor of 11.78 compared with the reference implementation. Copyright © 2011 John Wiley & Sons, Ltd.

Implementing shared memory consistency models on top of hardware caches gives rise to the well-known cache coherence problem. The standard solution involves implementing coherence protocols in hardware, an approach with some design complexity, hardware costs, and restrictions on interconnect behavior. However, for some memory consistency models, an alternative is to enforce coherence in the software implementation of synchronization primitives, using software controlled invalidations and forced writebacks. This requires minimal hardware support but gives less selective enforcement, which affects performance. This paper proposes a novel hybrid software-hardware coherence mechanism. In this scheme, software is responsible for triggering the coherence actions-self-invalidations and writebacks-at appropriate times while hardware uses Bloom filters to perform more selective self-invalidations. We evaluate the proposed scheme on applications from two different domains: the SPLASH-2 scientific and ALP multimedia benchmarks. Experimental results show that while the software-only coherence scheme shows less performance degradation than expected, it still unacceptably degrades performance for some of the benchmarks. Filtering out unnecessary invalidations improves the worst-case performance by as much as 93 percent, and brings the performance of the hybrid scheme within five percent of full hardware coherence for 10 out of 13 benchmarks, on a 32-core CMP with a shared L2 cache.

Data prefetching has long been an important technique to amortize the effects of the memory wall, and is likely to remain so in the current era of multi-core systems. Most prefetchers operate by identifying patterns and correlations in the miss address stream. Separating streams according to the memory access instruction that generates the misses is an effective way of filtering out spurious addresses from predictable streams. On the other hand, by localizing streams based on the memory access instructions, such prefetchers both lose the complete time sequence information of misses and can only issue prefetches for a single memory access instruction at a time.

Combined rendering of volume data and geometric objects (graphics objects) is important in a number of medical applications. Efficient generation of such rendering is required to support user interactions at a level that are useful in clinical applications. In this study, we describe the manipulation of graphics objects with volume data utilizing a widely used graphics API standard, OpenGL.>