As of now, especially in the benchmark mode, hashcat will not go to create and destroy context and command-queue for each enabled device each time it switches from one hash-mode to the next.
Specifically using OpenCL with an NVIDIA device, it was not possible to complete the benchmark because clCreateContext has memory leaks that slowly consume all available GPU memory until hashcat can activate a new context and disable the device.
Avoid deprecated HIP functions
All hipCtx* features have been declared deprecated, so we have replaced them with the new ones, also fixing a critical bug on handling multiple AMD devices in the same system.
Updated autotune to use the iteration count of the first user-defined hash instead of the self-test hash for slow hash tuning, assuming consistency across the hash list.
Updated autotune to prefer best-efficiency thread count only if it is at least 6% better than the max thread count, improving consistency in thread and accel values while allowing exceptions for special modes like 18600.
Changed default theoretical free memory by applying a reduction from max memory from 20% changed to 34%/. This happens only when runtime/OS cannot provide low-level free memory data.
Applied the same logic using --backend-keep-free percentage to host memory during early setup, when hashcat auto-reduces thread and accel counts to stay within limits, and that per compute device.
Changed terminal output from "Host memory required for this attack: ..." to "Host memory allocated for this attack: ...", and added free host memory as reference.
Fixed parameter types in inc_hash_blake2b.cl and inc_hash_blake2s.cl for FINAL value.
Added kernel code for -m 15400 to s04/s08/m04/m08, even if not needed, to help autotune find optimal workitem settings.
Fixed a rare autotune case (e.g. in mode 18600) where threads_min was not a multiple of kernel_preferred_wgs_multiple, and changes it so that as long as it only threads_min is affected and not threads_max, we now ensure at least kernel_preferred_wgs_multiple.
Improved autotune logic for best thread count: double thread count until reaching the device's preferred multiple, then increase in steps of that multiple while comparing efficiency vs. runtime, and select the configuration with best efficiency, not highest thread count.
Always set funnelshift support to true for HIP devices, as it always reports false.
Set minimum loop count to 250 for all VeraCrypt modes with PIM brute-force support.
There are cases where we fix the thread count in a kernel using
FIXED_LOCAL_SIZE, but when the runtime loads the kernel binary, it
reports that it can only execute it with a different thread count.
According to the OpenCL specification, this can happen due to register
pressure.
However, we fix the thread count for a specific reason, and we choose to
accept potential register spilling to global memory. A warning is now
issued to inform the user about the runtime's suggested thread count,
allowing them to override it via the command line if they encounter
issues.
Also fixed the thread count for -m 10700 on NVIDIA's OpenCL, where 4
bytes are always lost for an unknown reason (similar to the issue seen
in bcrypt).
- added support to 2D/3D Compute
- improved compute workloads calculation
Makefile:
- updated MACOSX_DEPLOYMENT_TARGET to 15.0
Unit tests:
- updated install_modules.sh with Crypt::Argon2
Argon2 start works with Apple Metal
Prepare Metal section in run_kernel() for 2D kernel invocation related to new salt->salt_dimy variable.
Move reusable Argon2 module code into separate file argon2_commit.c, similar to scrypt_commit.c, and update headers.
Update existing hash mode 34000 to use argon2_commit.c.
=====================================
This patch modifies the existing Argon2 plugin, which was initially
designed to work only with CUDA. Supporting OpenCL and HIP required
broader architectural changes.
1. The tmps[] structure no longer holds the "large buffer". This
buffer stored the scratch areas for all password candidates in one
chunk. But we do not need to hold scratch areas for all candidates
simultaneously. All we need to do is hold chunks large enough
per password.
To simplify logic, the buffer is not divided by password count, but
divided by four, which fits within the "1/4 global memory" limit on
some OpenCL runtimes.
Hashcat already had logic to support this, but the buffer needed to be
moved to a different buffer type. It has now been relocated from the
"tmp buffer" to the "extra tmp buffer", following the same strategy
used in newer SCRYPT plugins.
This improves handling across several subcomponents:
- Hashcat backend divides into four asymmetric buffers, hence the
name "4-buffer strategy"
- If the candidate count isn't divisible by 4, leftover candidates are
assigned to the first (and possibly second and third) buffer
- No code in the plugin is required, as this was designed for exactly
such cases where future algorithms require a lot of memory
- Plugin was rewritten to report the size needed in
module_extra_tmp_size(), which triggers the "4-buffer" strategy
- The split is not even, but each part is large enough to hold
a multiple of a full scratch buffer for a password
- The kernel code in m34000_init/loop/comp now uses a code block
that finds its buffer by doing "group_id % 4"
- Prevents the need to over-allocate memory to avoid OOB access
- The original "tmps buffer" now holds a small dummy state buffer
2. Replaced warp shuffle instruction
The instruction __shfl_sync() is not available in runtimes
other than CUDA. Some have alternatives, some do not.
To prevent branching per backend runtime, the new general macro
hc__shfl_sync() replaces all calls to __shfl_sync().
This allows us to implement runtime-specific solutions and
take effect at compile time to prevent regressions.
- CUDA:
We simply map to the original __shfl_sync()
- HIP:
We map to shfl(), a built-in intrinsic. This instruction doesn't
support masks like __shfl_sync() does, but masks are not needed
in Argon2 anyway. It requires an additional parameter, the wavefront
size. This is natively 64, but we hardcode this to 32 so it aligns
with NVIDIA's warp size.
- OpenCL:
- AMD: We have access to the instruction __builtin_amdgcn_ds_bpermute().
This instruction only supports 32-bit integers, requiring us to
pack and unpack the 64-bit values manually
- NVIDIA: We use inline assembly with "shfl.sync.idx.b32". Same as
with AMD, we need to pack and unpack 32-bit integers. The 64-bit
support in CUDA is just overloaded and internally does the same thing.
- Others: We use a shared memory pool and combine it with a barrier.
This LOCAL_VK pool must be sized at compile time and transported to
the Argon2 code in "inc_hash_argon2.cl". This required changing all
function declarations that use shuffles slightly.
Unlock full threading for init and comp kernels
===============================================
This is implemented using a new flag:
OPTS_TYPE_THREAD_MULTI_DISABLE
Behavior is similar to:
OPTS_TYPE_MP_MULTI_DISABLE
It simply disables the multiplier normally applied to password batch size.
But attention, this change completely unbinds this effect from the
real threads spawned on the compute device. If the thread count is not
set to 1 in the plugin, it will start autotuning it.
In the case of Argon2, we hard-code it to 32 instead, which also changes
how "warp size" was used in the original implementation, and which is not
compatible with HIP and/or OpenCL. However, we need to maintain this thread
size to utilize warp shuffle and its alternatives in other runtimes.
Benefits:
- Enables full threading for init and comp kernels (1667 H/s to 1722 H/s)
- Allows future algorithms to enable parallel processing of single
password candidates, if supported
Plugin changes:
- Removed the "hack" where thread count = 1 disabled the multiplier
- Removed per-device warp count detection code and struct changes
- Removed warp handling and "num_elements / thread_count" division in
the run_kernel() function
Simplified autotune logic for Argon2
====================================
The goal is to calculate the maximum number of password candidates that
can run in parallel, constrained only by device memory.
- Removed all code related to Argon2 from autotune
- Implemented in "module_extra_tuningdb_block()" (like SCRYPT)
- We create a tuningdb entry at runtime!
- Still allows override via tuningdb or CLI
- Considers register spilling (read at startup)
- Prevents global-to-host memory swap performance issues
Add Argon2I and ArgonD support
==============================
The kernel prepared from NFI already had support for the different Argon
types. No change was needed.
To support the other Argon2 types, the tokenizer had to be improved to
support a variety of different signatures in the same hash-mode.
Bugfixes
========
- Fixed missing entries in "switch_buffer_by_offset_8x4_le_S()"
- Fixed benchmark hash misdetection for scrypt. This was due to
outdated logic used in scrypt to detect whether the plugin was
called from a benchmark session or a regular one
- Fixed a bug in "module_hash_encode()" where Base64 padding '=' was
retained
- Fixed missing "GLOBAL_AS" / "PRIVATE_AS" casts for OpenCL
- Fixed compiler warnings (e.g., "index_u32x4()", "get_group_id()")
by adding return values
- Fixed a bug in token.len_max[6], which was allowing decoding
of a 256-byte data into a 16-byte buffer (digest)
Other improvements
==================
- Added unit test module for automated testing
- Added support to the tokenizer to allow multiple signatures.
Leave out TOKEN_ATTR_FIXED_LENGTH to enable this in your plugins
- Updated "hc_umulhi()", also exists for HIP
- Renamed "gid" to "bid" when using "get_group_id()" for clarity
- Removed "#ifdef IS_CUDA" as all backends are now supported
- Removed deprecated "OPTS_TYPE_MAXIMUM_ACCEL" attribute
Performance note
================
For testing, I used the self-test hash configured according to the
RFC 9106 recommendation: m=65536, t=3, p=1.
In my benchmarks, the AMD RX 7900 XTX achieved 1401 H/s using the same
hash that was used to test NVIDIA's RTX 4090. The RTX 4090 reached
1722 H/s, making it faster in absolute terms. However, at the time of
writing, it is more than three times as expensive as the 7900 XTX.
It's also worth noting that an older NVIDIA GTX 1080 Ti still reached
565 H/s with the same test vector, and may be found at significantly
lower cost.
Across all tested Argon2 configurations, the performance gap between
the RX 7900 XTX and the RTX 4090 remained proportionally consistent,
indicating a clear linear scaling relationship between the two GPUs.
Reduced the fixed memory reservation size from 1GiB to 64MiB as a result.
Added a warning when the user sets a thread count on the command line higher than recommended by the runtime (based on available registers and shared memory).
Added host-side logic to detect true funnel shift support and disable kernels using it if not supported on the device.
Updated more plugins to limit register count to 128 on NVIDIA GPUs.
Fixed a typedef issue for clEnqueueReadBuffer().
Updated Python/hcshared.py with missing entry for new salt_dimy attribute in salt_t struct.
Fixed a bug in the autotuner when determining the starting value for kernel loops, in cases where the iteration count is N-1 and not a multiple of 1024.
Updated additional plugins to use OPTI_TYPE_REGISTER_LIMIT.
