PATCH] Suricata: drop unused cuda HW acceleration
Commit Message
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>
---
config/suricata/suricata.yaml | 35 -----------------------------------
1 file changed, 35 deletions(-)
@@ -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
##