Predictive dynamic thermal management for multicore systems

ABSTRACT

Recently, processor power density has been increasing at an alarming rate resulting in high on-chip temperature. Higher temperature increases current leakage and causes poor reliability. In this paper, we propose a Predictive Dynamic Thermal Management (PDTM) based on Application-based Thermal Model (ABTM) and Core-based Thermal Model (CBTM) in the multicore systems. ABTM predicts future temperature based on the application specific thermal behavior, while CBTM estimates core temperature pattern by steady state temperature and workload. The accuracy of our prediction model is 1.6% error in average compared to the model in HybDTM [8], which has at most 5% error. Based on predicted temperature from ABTM and CBTM, the proposed PDTM can maintain the system temperature below a desired level by moving the running application from the possible overheated core to the future coolest core (migration) and reducing the processor resources (priority scheduling) within multicore systems. PDTM enables the exploration of the tradeoff between throughput and fairness in temperature-constrained multicore systems. We implement PDTM on Intel's Quad-Core system with a specific device driver to access Digital Thermal Sensor (DTS). Compared against Linux standard scheduler, PDTM can decrease average temperature about 10%, and peak temperature by 5°C with negligible impact of performance under 1%, while running single SPEC2006 benchmark. Moreover, our PDTM outperforms HRTM [10] in reducing average temperature by about 7% and peak temperature by about 3°C with performance overhead by 0.15% when running single benchmark.

References

1. "Intel 64 and IA-32 Architectures Software Developer's Manual," http://support.intel.com/design/processor/manuals/.
2. D. Bovet and M. Cesati, Understanding the Linux Kernel. O'Reilly Media, Inc, 2005.
3. D. Brooks and M. Martonosi, "Dynamic Thermal Management for High-Performance Microprocessors," in HPCA, 2001.
4. X. Chen, "Recursive Least-Squares Method with Membership Functions," in International Conference on Machine learning and Cybernetics, 2004.
5. S. Gunther, F. Binns, D. Carmean, and J. Hall, "Managing the Impact of increasing Microprocessor Power Consumption," Intel Technology Journal, 2001.
6. S. Heo, K. Barr, and K. Asanovic, "Reducing Power Density through Activity Migration," in ISLPED, 2003.
7. F. Kreith and M. S. Bohn, Principles of Heat Transfer. CENGAGE-Engineering, 2000.
8. A. Kumar, L. Shang, L.-S. Peh, and N. K. Jha, "HybDTM: A Coordinated Hardware-Software Approach for Dynamic Thermal Management," in DAC, 2006.
9. P. Michaud, A. Seznec, D. Fetis, Y. Sazeides, and T. Constantinou, "A Study of Thread Migration in Temperature-Constrained Multicores," ACM Transactions on Architecture and Code Optimization, vol. 4, no. 2, 2007.
10. M. D. Powell, M. Gomaa, and T. N. Vijaykumar, "Heat-and-Run: Leveraging SMT and CMP to Manage Power Density Through the Operating System," in ASPLOS, 2004.
11. K. Skadron, M. Stan, W. Huang, S. Velusamy, K. Sankaranarayanan, and D. Tarjan, "Temperature-Aware Microarchitecture: Modeling and Implementation," ACM TACO, vol. 1, no. 1, 2004.
12. K. Skadron, M. Stan, W. Huang, S. Velusamy, K. Sankaranarayanan, and D. Tarjan, "Temperature-Aware Microarchitecture," ISCA, 2003.
13. K. Skadron, "Hybrid Architectural Dynamic Thermal Management," in DATE, 2004.
14. J. Srinivasan and S. Adve, "Predictive Dynamic Thermal Management for Multimedia Applications," in ICS, 2003.
15. S. Wang and R. Bettati, "Reactive Speed Control in Temperature-Constrained Real-Time Systems," in ECRTS, 2006.

Index Terms

• Computer systems organization

  • Dependable and fault-tolerant systems and networks

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Networks

  • Network performance evaluation