Power Management for GPU-CPU Heterogeneous Systems
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Summary
GreenGPU is proposed, a holistic energy management framework for GPU-CPU heterogeneous architectures that dynamically splits and distributes workloads to GPU and CPU based on the workload characteristics, such that both sides can finish approximately at the same time.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 0
- References
- 17
- OpenAlex
- https://openalex.org/W41443797
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59798691
Keywords
Computer science, Central processing unit, CPU shielding, Energy consumption, Efficient energy use
References
- Power-Efficient Work Distribution Method for CPU-GPU Heterogeneous System
- An integrated GPU power and performance model
- Formal online methods for voltage/frequency control in multiple clock domain microprocessors
- Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations
- Rodinia: A benchmark suite for heterogeneous computing
- Variation-aware dynamic voltage/frequency scaling
- Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures
- Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping
- MapCG: Writing parallel program portable between CPU and GPU
- Meeting points: Using thread criticality to adapt multicore hardware to parallel regions
- Exploiting barriers to optimize power consumption of CMPs
- MapReduce: simplified data processing on large clusters
- The thrifty barrier: energy-aware synchronization in shared-memory multiprocessors
- Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures
- The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction
- The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction
- Power Consumption of GPUs from a Software Perspective
- Modeling Parallel System Workloads with Temporal Locality
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