Publications by Mahmut T. Kandemir

@inproceedings{abc,
	author = {Ashutosh Pattnaik and Xulong Tang and Adwait Jog and Onur Kayiran and Asit K. Mishra and Mahmut T. Kandemir and Onur Mutlu and Chita R. Das},
	booktitle = {Proceedings of the 2016 International Conference on Parallel Architectures and Compilation, PACT 2016, Haifa, Israel},
	title = {Scheduling Techniques for GPU Architectures with Processing-In-Memory Capabilities.},
	url = {http://doi.acm.org/10.1145/2967938.2967940},
	year = {2016}
}

@inproceedings{abc,
	author = {Onur Kayiran and Adwait Jog and Ashutosh Pattnaik and Rachata Ausavarungnirun and Xulong Tang and Mahmut T. Kandemir and Gabriel H. Loh and Onur Mutlu and Chita R. Das},
	booktitle = {Proceedings of the 2016 International Conference on Parallel Architectures and Compilation, PACT 2016, Haifa, Israel},
	title = {{\^I}{\textonequarter}C-States: Fine-grained GPU Datapath Power Management.},
	url = {http://doi.acm.org/10.1145/2967938.2967941},
	year = {2016}
}

@inproceedings{abc,
	author = {Adwait Jog and Onur Kayiran and Ashutosh Pattnaik and Mahmut T. Kandemir and Onur Mutlu and Ravishankar Iyer and Chita R. Das},
	booktitle = {Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science, Antibes Juan-Les-Pins, France},
	title = {Exploiting Core Criticality for Enhanced GPU Performance.},
	url = {http://doi.acm.org/10.1145/2896377.2901468},
	year = {2016}
}

@article{abc,
	abstract = {Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, different bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the cores. For example, when a GPU is bottlenecked by the available off-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive. 
This work describes the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides flexible mechanisms to automatically generate "assist warps" that execute on GPU cores to perform specific tasks that can improve GPU performance and efficiency. 
CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps. 
We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7\% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.},
	author = {Nandita Vijaykumar and Gennady Pekhimenko and Adwait Jog and Saugata Ghose and Abhishek Bhowmick and Rachata Ausavarungnirun and Chita R. Das and Mahmut T. Kandemir and Todd C. Mowry and Onur Mutlu},
	journal = {CoRR},
	title = {A Framework for Accelerating Bottlenecks in GPU Execution with Assist Warps.},
	url = {http://arxiv.org/abs/1602.01348},
	year = {2016}
}

@inproceedings{abc,
	author = {Rachata Ausavarungnirun and Saugata Ghose and Onur Kayiran and Gabriel H. Loh and Chita R. Das and Mahmut T. Kandemir and Onur Mutlu},
	booktitle = {2015 International Conference on Parallel Architecture and Compilation, PACT 2015, San Francisco, CA, USA},
	title = {Exploiting Inter-Warp Heterogeneity to Improve GPGPU Performance.},
	url = {http://dx.doi.org/10.1109/PACT.2015.38},
	year = {2015}
}

@inproceedings{abc,
	abstract = {Modern Graphics Processing Units (GPUs) are well provisioned to support the concurrent execution of thousands of threads. Unfortunately, diUerent bottlenecks during execution and heterogeneous application requirements create imbalances in utilization of resources in the 
 cores. For example, when a GPU is bottlenecked by the available on-chip memory bandwidth, its computational resources are often overwhelmingly idle, waiting for data from memory to arrive. This paper introduces the Core-Assisted Bottleneck Acceleration (CABA) framework that employs idle on-chip resources to alleviate different bottlenecks in GPU execution. CABA provides flexible mechanisms to automatically generate {\textquotedblleft}assist warps{\textquotedblright} that execute on GPU cores to perform specific tasks that can improve GPU performance and 
 efficiency. CABA enables the use of idle computational units and pipelines to alleviate the memory bandwidth bottleneck, e.g., by using assist warps to perform data compression to transfer less data from memory. Conversely, the same framework can be employed to handle cases where the GPU is bottlenecked by the available computational units, in which case the memory pipelines are idle and can be used by CABA to speed up computation, e.g., by performing memoization using assist warps. We provide a comprehensive design and evaluation of CABA to perform effective and flexible data compression in the GPU memory hierarchy to alleviate the memory bandwidth bottleneck. Our extensive evaluations show that CABA, when used to implement data compression, provides an average performance improvement of 41.7\% (as high as 2.6X) across a variety of memory-bandwidth-sensitive GPGPU applications.},
	author = {Nandita Vijaykumar and Gennady Pekhimenko and Adwait Jog and Abhishek Bhowmick and Rachata Ausavarungnirun and Chita R. Das and Mahmut T. Kandemir and Todd C. Mowry and Onur Mutlu},
	booktitle = {Proceedings of the 42nd Annual International Symposium on Computer Architecture},
	title = {A case for core-assisted bottleneck acceleration in GPUs: enabling flexible data compression with assist warps.},
	url = {http://doi.acm.org/10.1145/2749469.2750399},
	venue = {Portland, OR, USA},
	year = {2015}
}

@inproceedings{abc,
	author = {Onur Kayiran and Nachiappan Chidambaram Nachiappan and Adwait Jog and Rachata Ausavarungnirun and Mahmut T. Kandemir and Gabriel H. Loh and Onur Mutlu and Chita R. Das},
	booktitle = {47th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2014, Cambridge, United Kingdom},
	title = {Managing GPU Concurrency in Heterogeneous Architectures.},
	url = {http://dx.doi.org/10.1109/MICRO.2014.62},
	year = {2014}
}

@inproceedings{abc,
	author = {Adwait Jog and Onur Kayiran and Asit K. Mishra and Mahmut T. Kandemir and Onur Mutlu and Ravishankar Iyer and Chita R. Das},
	booktitle = {The 40th Annual International Symposium on Computer Architecture, ISCA{\textquoteright}13, Tel-Aviv, Israel},
	title = {Orchestrated scheduling and prefetching for GPGPUs.},
	url = {http://doi.acm.org/10.1145/2485922.2485951},
	year = {2013}
}

@inproceedings{abc,
	author = {Emre Kultursay and Mahmut T. Kandemir and Anand Sivasubramaniam and Onur Mutlu},
	booktitle = {2012 IEEE International Symposium on Performance Analysis of Systems  Software, Austin, TX, USA},
	title = {Evaluating STT-RAM as an energy-efficient main memory alternative.},
	url = {http://dx.doi.org/10.1109/ISPASS.2013.6557176},
	year = {2013}
}

@inproceedings{abc,
	author = {Adwait Jog and Onur Kayiran and Nachiappan Chidambaram Nachiappan and Asit K. Mishra and Mahmut T. Kandemir and Onur Mutlu and Ravishankar Iyer and Chita R. Das},
	booktitle = {Architectural Support for Programming Languages and Operating Systems, ASPLOS {\textquoteright}13, Houston, TX},
	title = {OWL: cooperative thread array aware scheduling techniques for improving GPGPU performance.},
	url = {http://doi.acm.org/10.1145/2451116.2451158},
	year = {2013}
}

@inproceedings{abc,
	author = {Nachiappan Chidambaram Nachiappan and Asit K. Mishra and Mahmut T. Kandemir and Anand Sivasubramaniam and Onur Mutlu and Chita R. Das},
	booktitle = {International Conference on Parallel Architectures and Compilation Techniques, PACT {\textquoteright}12, Minneapolis, MN},
	title = {Application-aware prefetch prioritization in on-chip networks.},
	url = {http://doi.acm.org/10.1145/2370816.2370886},
	year = {2012}
}

@inproceedings{abc,
	author = {Sai Prashanth Muralidhara and Lavanya Subramanian and Onur Mutlu and Mahmut T. Kandemir and Thomas Moscibroda},
	booktitle = {44rd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2011, Porto Alegre, Brazil},
	title = {Reducing memory interference in multicore systems via application-aware memory channel partitioning.},
	url = {http://doi.acm.org/10.1145/2155620.2155664},
	year = {2011}
}