12 citations · 36 across the 7 of their papers we have counts for
4 papers · 1 filter
Compilation Techniques for Graph Algorithms on GPUs
Ajay Brahmakshatriya, Yunming Zhang, Changwan Hong +3
The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of…
TIRAMISU: A Polyhedral Compiler for Dense and Sparse Deep Learning
Riyadh Baghdadi, Abdelkader Nadir Debbagh, Kamel Abdous +5
In this paper, we demonstrate a compiler that can optimize sparse and recurrent neural networks, both of which are currently outside of the scope of existing neural network compile…
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Charith Mendis, Alex Renda, Saman Amarasinghe +1
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and pe…
Weld: Rethinking the Interface Between Data-Intensive Applications
Shoumik Palkar, James Thomas, Deepak Narayanan +7
Data analytics applications combine multiple functions from different libraries and frameworks. Even when each function is optimized in isolation, the performance of the combined a…