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Juan M. Cebrian
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Publications
- 2020
- [j14]Juan M. Cebrian
, Lasse Natvig, Magnus Jahre
:
Scalability analysis of AVX-512 extensions. J. Supercomput. 76(3): 2082-2097 (2020) - 2018
- [j12]Abdullah Al Hasib, Juan M. Cebrian
, Lasse Natvig:
A vectorized k-means algorithm for compressed datasets: design and experimental analysis. J. Supercomput. 74(6): 2705-2728 (2018) - 2016
- [c15]Kameswar Rao Vaddina, Juan M. Cebrian
, Lasse Natvig:
Transient Temperature Prediction for Aging Thermal Sensors Using Artificial Neural Network. PDP 2016: 51-57 - 2015
- [j8]Juan M. Cebrian
, Magnus Jahre
, Lasse Natvig:
ParVec: vectorizing the PARSEC benchmark suite. Computing 97(11): 1077-1100 (2015) - [c13]Abdullah Al Hasib, Juan M. Cebrian
, Lasse Natvig:
V-PFORDelta: Data Compression for Energy Efficient Computation of Time Series. HiPC 2015: 416-425 - 2014
- [j6]Juan M. Cebrian
, Lasse Natvig, Jan Christian Meyer:
Performance and energy impact of parallelization and vectorization techniques in modern microprocessors. Computing 96(12): 1179-1193 (2014) - [c11]Juan M. Cebrian
, Magnus Jahre
, Lasse Natvig:
Optimized hardware for suboptimal software: The case for SIMD-aware benchmarks. ISPASS 2014: 66-75 - 2013
- [c10]Juan M. Cebrian
, Lasse Natvig:
Temperature effects on on-chip energy measurements. IGCC 2013: 1-6 - [c9]Jan Christian Meyer, Juan M. Cebrian
, Lasse Natvig, Vasileios Karakasis, Dimitris Siakavaras, Konstantinos Nikas:
Energy-Efficient Sparse Matrix Autotuning with CSX - A Trade-off Study. IPDPS Workshops 2013: 931-937 - 2012
- [c7]Juan M. Cebrian
, Lasse Natvig, Jan Christian Meyer:
Improving Energy Efficiency through Parallelization and Vectorization on Intel Core i5 and i7 Processors. SC Companion 2012: 675-684

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