et al.: Auto-tuning a high-level language targeted to gpu codes. In: 2012 Innovative Parallel Computing (InPar), pp 1–10, https://doi.org/10.1109/InPar.2012.6339595 (2012) Han, T.D., Abdelrahman, T.S.: hicuda: High-level gpgpu programming. IEEE Transact. Parall. Distribut. Syst...
Future developments will focus on fine-tuning the base model similar to how the NVIDIA engineers did on ChipNemo and refining the retrieval processes and integrating more advanced LLMs and embedding techniques as they become available. AI for HPC code development The integration o...
2.4 (Instruction) Fine-Tuning on Code These models apply Instruction Fine-Tuning techniques to enhance the capacities of Code LLMs. WizardCoder (StarCoder + Evol-Instruct): "WizardCoder: Empowering Code Large Language Models with Evol-Instruct" [2023-06] [ICLR 2024] [paper] [repo] PanGu-Co...
Sharma, T., et al.: A survey on machine learning techniques for source code analysis. arXiv preprint arXiv:2110.09610 (2021) Google: ML-enhanced code completion improves developer productivity. https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html GitHub: GitHub CoPilot....
Prior to Salesforce, he worked as a Sr Software & Process Automation Engineer for numerous years. He has a M.S. in Computer Science with a focus on high performance computing & back-end distributed systems from the University of Chicago and a B.S.E. in Materials Science & Engineering ...
The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time...
Parallelism divides a workload into smaller tasks that are run in parallel. You can achieve parallelism by using techniques like multiprocessing or distributed computing. Distribute tasks across multicore processors to optimize workload management. Optimize code to take advantage of the CPU architecture,...
Conclusion: Fine-Tuning CI for Developer Happiness By leveraging caching, autoscaling, bin packing, NVMe disks, and capacity reservation, we’ve significantly improved both developer experience and operational efficiency. The outcome of this, along with the overall migration of runners to Kubernetes, ...
3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization no code implementations • 2 May 2022 • Jim James, Nathan Pruyne, Tiberiu Stan, Marcus Schwarting, Jiwon Yeom, Seungbum Hong, Peter Voorhees, Ben Blaiszik, Ian Foster The trained 3D...
Epshteyn A,Garzaran M.Analytic models and empirical search:A hybrid approach to code optimization. Proc 18th International Workshop on Languages and Compilers for Parallel Computing . 2005Analytic models and empirical search: A hybrid approach to code optimization - Epshteyn, Garzaran, et al. -...