Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeLTA: GPU Performance Model for Deep Learning Applications with
  In-depth Memory System Traffic Analysis   Training convolutional neural networks (CNNs) requires intense compute
throughput and high memory bandwidth. Especially, convolution layers account
for the majority of the execution time of CNN training, and GPUs are commonly
used to accelerate these layer workloads. GPU design optimization for efficient
CNN training acceleration requires the accurate modeling of how their
performance improves when computing and memory resources are increased. We
present DeLTA, the first analytical model that accurately estimates the traffic
at each GPU memory hierarchy level, while accounting for the complex reuse
patterns of a parallel convolution algorithm. We demonstrate that our model is
both accurate and robust for different CNNs and GPU architectures. We then show
how this model can be used to carefully balance the scaling of different GPU
resources for efficient CNN performance improvement.

37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an