1. [Publications](/publications) 2. A Comparative Analysis of Microarchitecture Effects on CPU and GPU Memory System Behavior # A Comparative Analysis of Microarchitecture Effects on CPU and GPU Memory System Behavior ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image") While heterogeneous CPU/GPU systems have been traditionally implemented on separate chips, each with their own private DRAM, heterogeneous processors are integrating these different core types on the same die with access to a common physical memory. Further, emerging heterogeneous CPU-GPU processors promise to offer tighter coupling between core types via a unified virtual address space and cache coherence. To adequately address the potential opportunities and pitfalls that may arise from this tighter coupling, it is important to have a deep understanding of application- and memory-level demands from both CPU and GPU cores. This paper presents a detailed comparison of memory access behavior for parallel applications executing on each core type in tightly-controlled heterogeneous CPU-GPU processor simulation. This characterization indicates that applications are typically designed with similar algorithmic structures for CPU and GPU cores, and each core type’s memory access path has a similar locality filtering role. However, the different core and cache microarchitectures expose substantially different memory-level parallelism (MLP), which results in different instantaneous memory access rates and sensitivity to memory hierarchy architecture. ## Authors Joel Hestness (University of Wisconsin) [Steve Keckler](/person/stephen-keckler) David A. Wood (University of Wisconsin) ## Publication Date Sunday, October 26, 2014 ## Published in [International Symposium on Workload Characterization (IISWC)](https://ieeexplore.ieee.org/document/6983054) ## Research Area [Computer Architecture](/research-area/computer-architecture) ## External Links [IEEE Digital Library](https://ieeexplore.ieee.org/document/6983054) ## Uploaded Files [Published manuscript](https://d1qx31qr3h6wln.cloudfront.net/publications/IISWC_2014_GPU_memory_characterization.pdf "Open file in new window")683.34 KB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .