showing 2 related works from this author
And Now for Something Completely Different: Running Lisp on GPUs
2018
The internal parallelism of compute resources increases permanently, and graphics processing units (GPUs) and other accelerators have been gaining importance in many domains. Researchers from life science, bioinformatics or artificial intelligence, for example, use GPUs to accelerate their computations. However, languages typically used in some of these disciplines often do not benefit from the technical developments because they cannot be executed natively on GPUs. Instead existing programs must be rewritten in other, less dynamic programming languages. On the other hand, the gap in programming features between accelerators and common CPUs shrinks permanently. Since accelerators are becomi…
VarySched: A Framework for Variable Scheduling in Heterogeneous Environments
2016
Despite many efforts to better utilize the potential of GPUs and CPUs, it is far from being fully exploited. Although many tasks can be easily sped up by using accelerators, most of the existing schedulers are not flexible enough to really optimize the resource usage of the complete system. The main reasons are (i) that each processing unit requires a specific program code and that this code is often not provided for every task, and (ii) that schedulers may follow the run-until-completion model and, hence, disallow resource changes during runtime. In this paper, we present VarySched, a configurable task scheduler framework tailored to efficiently utilize all available computing resources in…