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Suricata: drop unused cuda HW acceleration

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As stated in https://bugzilla.ipfire.org/show_bug.cgi?id=11808#c5 ,
Cuda hardware acceleration is unused and so the configuration file
section can be removed.

This partially addresses #11808.

Signed-off-by: Peter Müller <peter.mueller@link38.eu>
Cc: Stefan Schantl <stefan.schantl@ipfire.org>
Signed-off-by: Stefan Schantl <stefan.schantl@ipfire.org>
This commit is contained in:
Peter Müller
2019-01-23 21:22:41 +01:00
committed by Stefan Schantl
parent 68699ecfff
commit 8059239661
1 changed files with 0 additions and 35 deletions
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35
config/suricata/suricata.yaml
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@@ -933,41 +933,6 @@ profiling:
filename: pcaplog_stats.log
append: yes
##
## Hardware accelaration
##
# Cuda configuration.
cuda:
# The "mpm" profile. On not specifying any of these parameters, the engine's
# internal default values are used, which are same as the ones specified in
# in the default conf file.
mpm:
# The minimum length required to buffer data to the gpu.
# Anything below this is MPM'ed on the CPU.
# Can be specified in kb, mb, gb. Just a number indicates it's in bytes.
# A value of 0 indicates there's no limit.
data-buffer-size-min-limit: 0
# The maximum length for data that we would buffer to the gpu.
# Anything over this is MPM'ed on the CPU.
# Can be specified in kb, mb, gb. Just a number indicates it's in bytes.
data-buffer-size-max-limit: 1500
# The ring buffer size used by the CudaBuffer API to buffer data.
cudabuffer-buffer-size: 500mb
# The max chunk size that can be sent to the gpu in a single go.
gpu-transfer-size: 50mb
# The timeout limit for batching of packets in microseconds.
batching-timeout: 2000
# The device to use for the mpm. Currently we don't support load balancing
# on multiple gpus. In case you have multiple devices on your system, you
# can specify the device to use, using this conf. By default we hold 0, to
# specify the first device cuda sees. To find out device-id associated with
# the card(s) on the system run "suricata --list-cuda-cards".
device-id: 0
# No of Cuda streams used for asynchronous processing. All values > 0 are valid.
# For this option you need a device with Compute Capability > 1.0.
cuda-streams: 2
##
## Include other configs
##