Mars: a MapReduce framework on graphics processors
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Clustering billions of data points using GPUs
Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop
The Scalable Heterogeneous Computing (SHOC) benchmark suite
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
An integrated GPU power and performance model
Proceedings of the 37th annual international symposium on Computer architecture
Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU
Proceedings of the 37th annual international symposium on Computer architecture
Performance and Power Analysis of ATI GPU: A Statistical Approach
NAS '11 Proceedings of the 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage
The tradeoffs of fused memory hierarchies in heterogeneous computing architectures
Proceedings of the 9th conference on Computing Frontiers
Ameliorating memory contention of OLAP operators on GPU processors
DaMoN '12 Proceedings of the Eighth International Workshop on Data Management on New Hardware
GreenGPU: A Holistic Approach to Energy Efficiency in GPU-CPU Heterogeneous Architectures
ICPP '12 Proceedings of the 2012 41st International Conference on Parallel Processing
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More than 90% of consumer computers use integrated graphics processors. In these processors, the CPU and the GPU share the same physical memory. Due to high density, good power efficiency, and low cost, integrated graphics processors are promising candidates for next-generation micro-servers and, hence, data-center workloads. While discrete graphics processors have been extensively studied, there is very little work on characterizing integrated GPUs. This paper is a step towards understanding the power and performance of integrated GPUs. Our results reveal many architectural caveats that programmers need to be aware of to exploit integrated GPUs: memory contention between the CPU and GPU, workload dependent energy efficiency, and data transfer tradeoffs.