- NVIDIA CUDA TOOLKIT REQUIRED TO USE VIDEO CARD PASSWORD
- NVIDIA CUDA TOOLKIT REQUIRED TO USE VIDEO CARD CRACK
But with one noticeable difference, it was running at half the speed I was getting with Open CL. These can be installed side by side, right? I restarted the command shell and restarted Hashcat 6.25 and it worked. So I downloaded and installed CUDA 11, but on top of CUDA 8. For some reason it was failing to load and 6.2.5 requires CUDA 11. It doesn't work with GTX 560 Ti at all and it almost works with GTX 1650. With version 5.0 I saw a good improvement, maxing it out at 1835 MH/s.īut version 6.2.5 was still beyond my reach.
Then I moved on to 4.0 version and my hashrate started to decline, quite significantly. I decided to start with 3.0 because I was having great success with that version using old GPUs.
Compare that with 31 minutes 16 seconds on Radeon HD 6870, and 3 hour, 39 minutes and 15 seconds on Intel UHD 630. An MD5 job that took 1 hour, 23 minutes and 29 seconds on GTX 560 Ti was now taking only 12 minutes and 22 seconds on GTX 1650. This is a huge improvement for my limited resources. I was getting up to 6500 MH/s, relying only on Open CL, no CUDA runtime. I waited for 10 minutes before I aborted.īefore trying to run 6.2.5, I was running 3.0 with flying colors. Initializing backend runtime for device #1. Watchdog: Temperature abort trigger set to 90c See the above message to find out about the exact limits. If you want to switch to optimized kernels, append -O to your commandline.
NVIDIA CUDA TOOLKIT REQUIRED TO USE VIDEO CARD CRACK
Pure kernels can crack longer passwords, but drastically reduce performance. Hashes: 7 digests 7 unique digests, 1 unique saltsīitmaps: 16 bits, 65536 entries, 0x0000ffff mask, 262144 bytes, 5/13 rotatesĪTTENTION! Pure (unoptimized) backend kernels selected.
NVIDIA CUDA TOOLKIT REQUIRED TO USE VIDEO CARD PASSWORD
Maximum password length supported by kernel: 256 Minimum password length supported by kernel: 0 OpenCL API (OpenCL 3.0 CUDA 11.5.121) - Platform #1
This may cause "CL_OUT_OF_RESOURCES" or related errors. * Device #1: WARNING! Kernel exec timeout is not disabled. * Device #1: CUDA SDK Toolkit not installed or incorrectly installed.ĬUDA SDK Toolkit required for proper device support and utilization. Successfully initialized NVIDIA CUDA library.
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