@misc {Simula.simula.2322,
	title = {Mint: a User-Friendly C-to-CUDA Code Translator},
	howpublished = {Talk given at SIAM CSE{\textquoteright}13, February 25},
	year = {2013},
	abstract = {Aiming at automated source-to-source code translation from C to CUDA, we have developed the Mint framework. Users only need to annotate serial C code with a few compiler directives, specifying host-device data transfers plus the parallelization depth and granularity of loop nests. Mint then generates CUDA code as output, while carrying out on-chip memory optimizations that will greatly benefit 3D stencil computations. Several real-world applications have been ported to GPU using Mint.},
	keywords = {Conference},
	author = {Cai, Xing and Unat, Didem and Baden, Scott}
}
@article {Simula.simula.2480,
	title = {Modeling and Predicting Performance of High Performance Computing Applications on Hardware Accelerators},
	journal = {International Journal of High Performance Computing Applications},
	volume = {27},
	number = {2},
	year = {2013},
	month = {May},
	pages = {89-108},
	abstract = {Hybrid-core systems speedup applications by offloading certain compute operations that can run faster on hardware accelerators. However, such systems require significant programming and porting effort to gain a performance benefit from the accelerators. Therefore, prior to porting it is prudent to investigate the predicted performance benefit of accelerators for a given workload. To address this problem we present a performance-modeling framework that predicts the application performance rapidly and accurately for hybrid-core systems. We present predictions for two full-scale HPC applications-HYCOM and Milc. Our results for two accelerators (GPU and FPGA) show that gather/scatter and stream operations can speedup by as much as a factor of 15 and overall compute time of Milc and HYCOM improve by 3.4\% and 20\%, respectively. We also show that in order to benefit from the accelerators, 70\% of the latency of data transfer time between the CPU and the accelerators needs to be overcome.},
	doi = {10.1177/1094342012468180},
	author = {Mitesh R. {Meswani} and Carrington, Laura and Unat, Didem and Snavely, Allan and Baden, Scott and Poole, Stephen}
}
@article {Simula.sc.1270,
	title = {Accelerating a 3D Finite-Difference Earthquake Simulation With a C-to-CUDA Translator},
	journal = {Computing in Science \& Engineering},
	volume = {14},
	number = {3},
	year = {2012},
	pages = {48-59},
	doi = {http://dx.doi.org/10.1109/MCSE.2012.44},
	author = {Unat, Didem and Zhou, Jun and Cui, Yifeng and Baden, Scott and Cai, Xing}
}
@phdthesis {Simula.simula.1736,
	title = {Domain-Specific Translator and Optimizer for Massive On-Chip Parallelism},
	year = {2012},
	school = {University of California, San Diego},
	type = {phd},
	author = {Unat, Didem}
}
@inproceedings {Simula.sc.1282,
	title = {Interactive Data-Centric Viewpoint Selection},
	journal = {Proceedings of Conference on Visualization and Data Analysis},
	year = {2012},
	publisher = {SPIE},
	type = {Conference},
	keywords = {Conference},
	doi = {10.1117/12.907480},
	author = {Han Suk {Kim} and Unat, Didem and Baden, Scott and J. P. {Schulze}}
}
@inproceedings {Simula.simula.1734,
	title = {Modeling and Predicting Performance of High Performance Computing Applications on Hardware Accelerators},
	journal = {Proceedings of IEEE 26th International Parallel and Distributed Processing Symposium Workshops \& PhD Forum},
	year = {2012},
	pages = {1828-1837},
	publisher = {IEEE Computer Society},
	type = {Workshop},
	keywords = {Workshop},
	doi = {10.1109/IPDPSW.2012.226},
	author = {Mitesh, Meswani and Carrington, Laura and Unat, Didem and Snavely, Allan and Baden, Scott and Poole, Stephen}
}
@inproceedings {Simula.sc.1280,
	title = {Auto-Optimization of a Feature Selection Algorithm},
	journal = {Proceedings of the 4th Workshop on Emerging Applications and Many-core Architecture},
	year = {2011},
	type = {Workshop},
	author = {Unat, Didem and Han Suk {Kim} and J. P. {Schulze} and Baden, Scott}
}
@inproceedings {Simula.simula.388,
	title = {Mint: Realizing CUDA Performance in 3D Stencil Methods With Annotated C},
	journal = {Proceedings of the 25th International Conference on Supercomputing (ICS{\textquoteright}11)},
	year = {2011},
	pages = {214-224},
	publisher = {ACM Press},
	type = {Conference},
	abstract = {We present Mint, a programming model that enables the non-expert to enjoy the performance benefits of hand coded CUDA without becoming entangled in the details. Mint targets stencil methods, which are an important class of scientific applications. We have implemented the Mint programming model with a source-to-source translator that generates optimized CUDA C from traditional C source. The translator relies on annotations to guide translation at a high level. The set of pragmas is small, and the model is compact and simple. Yet, Mint is able to deliver performance competitive with painstakingly hand-optimized CUDA. We show that, for a set of widely used stencil kernels in two and three dimensions, Mint realized 80\% of the performance obtained from aggressively optimized CUDA on the 200 series NVIDIA GPUs. Our optimizations target three dimensional kernels, which present a daunting array of optimizations.},
	isbn = {978-1-4503-0102-2},
	doi = {10.1145/1995896.1995932},
	author = {Unat, Didem and Cai, Xing and Baden, Scott},
	editor = {David K. {Lowenthal} and Bronis R. {de Supinski} and Sally A. {McKee}}
}
@misc {Simula.simula.306,
	title = {Accelerating Finite Difference Method on Graphic Processors (GPUs)},
	howpublished = {CBC Seminar on GPU Programming and Computing - May 4},
	year = {2010},
	author = {Unat, Didem}
}
@misc {Simula.sc.860,
	title = {Detailed Numerical Analyses of the Aliev-Panfilov Model on GPGPU},
	howpublished = {Talk at PARA2010 Conference},
	year = {2010},
	author = {Cai, Xing and Unat, Didem and Baden, Scott}
}
@misc {Simula.simula.304,
	title = {Introduction to Programming a GPU With CUDA and a Case Study: Accelerating Stencil Computation With GPUs},
	year = {2010},
	author = {Unat, Didem and Baden, Scott}
}
@misc {Simula.acdc.8,
	title = {Optimizing the Aliev-Panfilov Model of Cardiac Excitation on Heterogeneous Systems},
	howpublished = {Talk at Para 2010: State of the Art in Scientific and Parallel Computing in Reykjavik on June 6-9, 2010},
	year = {2010},
	month = {June},
	abstract = {The Aliev-Panfilov model is a simple model for signal propagation in cardiac tissue, and accounts for complex behavior such as how spiral waves break up and form elaborate patterns. Spiral waves can lead to life-threatening situations such as ventricular fibrillation. We discuss an implementation and underlying optimizations for the nVIDIA Tesla C1060 GPU as well as an implementation on multiple GPUs running under MPI. We achieve nearly perfect scaling on 4 GPUs, in single precision, running 58 times faster than a CPU-only implementation and 26 times faster in double precision.},
	author = {Unat, Didem and Cai, Xing and Baden, Scott}
}
@misc {Simula.simula.308,
	title = {Revisiting Finite Element Matrix Assembly},
	howpublished = {AC/DC seminar series},
	year = {2010},
	author = {Unat, Didem}
}
@inproceedings {Simula.SC.432,
	title = {An Adaptive Sub-Sampling Method for In-Memory Compression of Scientific Data},
	journal = {Data Compression Conference},
	year = {2009},
	publisher = {IEEE Computer Society Press},
	type = {Conference},
	abstract = {A current challenge in scientific computing is how to curb the growth of simulation datasets without losing valuable information. While wavelet based methods are popular, they require that data be decompressed before it can analyzed, for example, when identifying time-dependent structures in turbulent flows. We present Adaptive Coarsening, an adaptive subsampling compression strategy that enables the compressed data product to be directly manipulated in memory without requiring costly decompression. We demonstrate compression factors of up to 8 in turbulent flow simulations in three dimensions. Our compression strategy produces a non-progressive multiresolution representation, subdividing the dataset into fixed sized regions and compressing each region independently.},
	isbn = {978-0-7695-3592-0},
	author = {Unat, Didem and Theodore {Hromadka, III} and Baden, Scott},
	editor = {IEEE Computer S {Press}}
}
@misc {Simula.sc.666,
	title = {Optimizations of Common Scientific Kernels on GPU},
	howpublished = {Poster presented at the Early Adopters PhD Workshop: Building the Next Generation of Application Scientists, Supercomputing Conference 2009, Portland, OR, USA},
	year = {2009},
	author = {Unat, Didem and Baden, Scott}
}
