| * |
| *n*x | Race-Condition-Robust Hardware-Software Equivalence in *n*x |
| a |
| abstraction | Concurrency and Models of Abstraction: Past, Present and Future |
| anomaly detection | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| c |
| CNN | Evaluation of FPGA Acceleration of Neural Networks |
| concurrency | Concurrency and Models of Abstraction: Past, Present and Future |
| CSP | Slurm Scheduling From Rules-Based Systems
Concurrency and Models of Abstraction: Past, Present and Future |
| cuBLAS (Cuda Basic Linear Algebra Subprograms) | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| CUDA API | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| cuFFT (Cuda Fast Fourier Transforms) | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| CULA (Cuda Linear Algebra) | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| Cyber Terrorism | Concurrency and Models of Abstraction: Past, Present and Future |
| d |
| Data Lake | Concurrency and Models of Abstraction: Past, Present and Future |
| data provenance report | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| Discrete Fourier Transform (DFT) | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| e |
| Electrical Engineering | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| f |
| fanotify | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| FPGA | Evaluation of FPGA Acceleration of Neural Networks |
| frequency domain analysis | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| g |
| GPU computing | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| h |
| hardware-software equivalence | Race-Condition-Robust Hardware-Software Equivalence in *n*x |
| HLS | Evaluation of FPGA Acceleration of Neural Networks |
| i |
| inotify | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| l |
| Linux | Race-Condition-Robust Hardware-Software Equivalence in *n*x |
| m |
| Map Reduce | Concurrency and Models of Abstraction: Past, Present and Future |
| meow | Slurm Scheduling From Rules-Based Systems
Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| Microservices | Concurrency and Models of Abstraction: Past, Present and Future |
| p |
| parallel computing | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| perf | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| performance optimization | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| q |
| quantum computing | Concurrency and Models of Abstraction: Past, Present and Future |
| r |
| race condition | Race-Condition-Robust Hardware-Software Equivalence in *n*x |
| Radio Signal Detection | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| Rules-based | Slurm Scheduling From Rules-Based Systems |
| s |
| signal processing | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| Slurm | Slurm Scheduling From Rules-Based Systems |
| SME | Evaluation of FPGA Acceleration of Neural Networks |
| SSH | Race-Condition-Robust Hardware-Software Equivalence in *n*x |
| strace | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
| t |
| triangulation | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| w |
| wireless communication | Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing |
| Workflow Manager | Is it feasible to identify outputs of an arbitrary process at run time without excessively slowing down workflows? |
