Hybrid parallelization strategies for machine learning programs on top of MapReduce are provided. In one embodiment, a method of and computer program product for parallel execution of machine learning programs are provided. Program code is received. The program code contains at least one parallel for...
In the message-passing programming model, tasks have private memories, and they communicate explicitly via message exchange. To exchange a message, each sends operation needs to have a corresponding receive operation. Tasks are not constrained to exist on the same physical machine. ...
Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments. python workflow data-science machine-learning deep-learning hpc quantum parallelization pipelines orchestration quantum-computing machinelearning covalent workflow-management hacktoberfest...
Machine learning tools and automaton free clinical project managers from tedious, repetitive activities so they can focus on strategic activities, drive optimal proactive planning in study execution, and aid in-depth internal reviews of organizational processes, resource allocations,study costs...
Paper tables with annotated results for Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms
A potential solution to this issue may be identified through a thorough review of the existing literature in this domain. Control-flow paradigm basics The control-flow paradigm is rooted in the principles established by the von Neumann architecture. Initially, this paradigm is exemplified by single-...
Infrastructure. EasyDist decouples auto-parallel algorithms from specific machine learning frameworks and IRs. This design choice allows for the development and benchmarking of different auto-parallel algorithms in a more flexible manner, leveraging the capabilities and abstractions provided by EasyDist. ...
2.1Machine learning targeting Intel Xeon Phi In this section, we discuss existing work for support vector machines (SVMs), restricted Boltzmann machines (RBMs), sparse auto encoders and the brain-state-in-a-box (BSB) model. You et al. [59] present a library for parallel support vector mach...
These parameters are used by neural networks to perform machine learning tasks when processing inputs. To speed up inference, we develop Partition Pruning, an innovative scheme to reduce the parameters used while taking into consideration parallelization. We evaluated the performance and energy ...
In the latter case, a high recognition rate is expected on a vocabulary of small to medium size. To achieve this goal, the model must be refined. Thus, both the training stage and the recognition stage for such applications can be very time consuming and occasional re-training may happen....