Xbox 360 System Architecture

Andrews, Jacob A; Baker, Nick

doi:10.1109/mm.2006.45

Cited by 75 publications

(21 citation statements)

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“…The Xbox 360 features a custom built IBM PowerPC-based CPU with three 3.2 GHz core processors. The Xbox GPU by ATI houses 48 parallel shaders with unified architecture and 10 MB of embedded DRAM (EDRAM) [25], with 512 MB of DRAM as main memory. The CPU and GPU of the Xbox 360 are customized for graphics-intensive computation, with the GPU able to read directly from the CPU L2 cache.…”

Section: Quantitative Resultsmentioning

confidence: 99%

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Wilson

Banzhaf

2010

Genet Program Evolvable Mach

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We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console by using a publicly available common framework for the devices called XNA (for ''XNA's Not Acronymed''). By constructing the LGP within this framework, we effectively produce an LGP ''game'' for PC and XBox 360 that displays results as they evolve. We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described. Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the metrics of GP operations per second, actual time elapsed, This work is based on an earlier work: Deployment

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Section: Quantitative Resultsmentioning

confidence: 99%

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Wilson

Banzhaf

2010

Genet Program Evolvable Mach

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“…Hardware tessellation [Moreton 2001;Andrews and Baker 2006;Microsoft Corporation 2009] eliminates the two-pass problem of traditional GPGPU methods by evaluating and rasterizing the surface geometry directly on-chip, thus minimizing global memory I/O. That is, meshes are interpreted as a set of parametric patches, with each patch defined by a fixed number of control points [Schäfer et al 2014].…”

Section: Patch Evaluation Using Hardware Tessellationmentioning

confidence: 99%

Dynamic feature-adaptive subdivision

Schäfer

Raab

Keinert

et al. 2015

Proceedings of the 19th Symposium on Interactive 3D Graphics and Games

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Figure 1: Rendering of the Frog model (left), using feature-adaptive subdivision [Nießner et al. 2012a] takes 0.68ms (middle); our method only takes 0.36ms by performing locally adaptive subdivision (right); colors denote different subdivision levels. AbstractFeature-adaptive subdivision (FAS) is one of the state-of-the art real-time rendering methods for subdivision surfaces on modern GPUs. It enables efficient and accurate rendering of subdivision surfaces in many interactive applications, such as video games or authoring tools. In this paper, we present dynamic feature-adaptive subdivision (DFAS), which improves upon FAS by enabling an independent subdivision depth for every irregularity. Our subdivision kernels fill a dynamic patch buffer on-the-fly with the appropriate number of patches corresponding to the chosen levelof-detail scheme. By reducing the number of generated and processed patches, DFAS significantly improves upon the performance of static FAS.

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“…For this, more independent CPUs, bigger caches, and more independent memory controllers have been used, and it is possible to find many applications that use heterogeneous processors with several memory controllers to provide a large memory interface. One can find an example of such architecture on the Xbox360 [4]. Fig.…”

Section: Fig-1 Noc Topologymentioning

confidence: 99%

An Efficient Way to Increase Performance by Using Low Power Reconfigurable Routers

Sharma¹

2013

IOSR-JECE

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Xbox 360 System Architecture

Cited by 75 publications

References 0 publications

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Dynamic feature-adaptive subdivision

An Efficient Way to Increase Performance by Using Low Power Reconfigurable Routers

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