scholarly journals MOOSE Framework Speed and Memory Optimizations

2017 ◽  
Author(s):  
R. W. Carlsen ◽  
C. J. Permann
Keyword(s):  
Memory Optimizations

Memory Optimizations for Sparse Linear Algebra on GPU Hardware

2021 ◽  
Author(s):  
Aaron Walden ◽  
Mohammad Zubair ◽  
Christopher P. Stone ◽  
Eric J. Nielsen
Keyword(s):  
Linear Algebra ◽  
Sparse Linear Algebra ◽  
Memory Optimizations

scholarly journals An SMT Encoding of LLVM’s Memory Model for Bounded Translation Validation

2021 ◽  
pp. 752-776
Author(s):  
Juneyoung Lee ◽  
Dongjoo Kim ◽  
Chung-Kil Hur ◽  
Nuno P. Lopes
Keyword(s):  
Procedural Memory ◽  
Automatic Verification ◽  
Memory Model ◽  
Translation Validation ◽  
Verification Tools ◽  
Memory Optimizations ◽  
Specification Document ◽  
Validation Tool

AbstractSeveral automatic verification tools have been recently developed to verify subsets of LLVM’s optimizations. However, none of these tools has robust support to verify memory optimizations.In this paper, we present the first SMT encoding of LLVM’s memory model that 1) is sufficiently precise to validate all of LLVM’s intra-procedural memory optimizations, and 2) enables bounded translation validation of programs with up to hundreds of thousands of lines of code. We implemented our new encoding in Alive2, a bounded translation validation tool, and used it to uncover 21 new bugs in LLVM memory optimizations, 10 of which have been already fixed. We also found several inconsistencies in LLVM IR’s official specification document (LangRef) and fixed LLVM’s code and the document so they are in agreement.

scholarly journals A Survey on System-Level Design of Neural Network Accelerators

2021 ◽  
Vol 16 (2) ◽  
pp. 1-10
Author(s):  
Kenshu Seto
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
System Level ◽  
System Level Design ◽  
Future Directions ◽  
Recent Trends ◽  
Memory Optimizations ◽  
Loop Optimizations ◽  
Accelerator Design ◽  
Nested Loop

In this paper, we present a brief survey on the system-level optimizations used for convolutional neural network (CNN) inference accelerators. For the nested loop of convolutional (CONV) layers, we discuss the effects of loop optimizations such as loop interchange, tiling, unrolling and fusion on CNN accelerators. We also explain memory optimizations that are effective with the loop optimizations. In addition, we discuss streaming architectures and single computation engine architectures that are commonly used in CNN accelerators. Optimizations for CNN models are briefly explained, followed by the recent trends and future directions of the CNN accelerator design.

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