Installation#

GLASS is a header-only CUDA library. There is nothing to build or link for the pure-SIMT path — you add the repository root to your include path and #include the umbrella header. Only the optional glass::nvidia:: backend pulls in external dependencies (NVIDIA MathDx).

Header-only include model#

Clone (or vendor) the repository, add its root to your compiler’s include path, and include the header you need:

#include "glass.cuh"        // pure-SIMT glass:: API
// or
#include "glass-cgrps.cuh"  // cooperative-groups glass::cgrps:: API
// or
#include "glass-nvidia.cuh" // CUB / cuBLASDx / cuSOLVERDx backend

A minimal nvcc invocation for the pure-SIMT path:

nvcc -std=c++17 -I /path/to/GLASS -arch=sm_86 my_kernel.cu -o my_kernel

CUDA Toolkit requirement#

CUDA Toolkit 11.0 or newer. CUB (used by the glass::nvidia:: L1 reductions) ships bundled with every CUDA 11+ install — no separate download.
C++17 (nvcc -std=c++17). The pure-SIMT core compiles cleanly under C++17; the glass::nvidia:: path requires C++17 (mandated by cuBLASDx / cuSOLVERDx).

Table 1 Per-component requirements#
Component	C++ Standard	Optional deps
`glass.cuh`, `glass-cgrps.cuh`	C++17	—
`glass-nvidia.cuh` (L1 only)	C++17	CUB (bundled with CUDA 11+)
`glass-nvidia.cuh` (L2/L3 GEMM/GEMV/batched)	C++17 + `--expt-relaxed-constexpr`	cuBLASDx
`glass-nvidia.cuh` (LAPACK)	C++17 + `--expt-relaxed-constexpr` + cuSOLVERDx link flags	cuSOLVERDx

Optional: MathDx setup for the `glass::nvidia::` backend#

The glass::nvidia:: GEMM/GEMV/batched and LAPACK wrappers route to NVIDIA’s device-side libraries cuBLASDx and cuSOLVERDx, which ship together in the MathDx package. They are not distributed via apt — installation is a manual download from the NVIDIA Developer portal.

Table 2 MathDx libraries#
Library	Used by	Header-only?
CUB	`glass::nvidia::reduce` / `dot` / `nrm2`	Yes (bundled with CUDA)
cuBLASDx	`gemv`, `gemm`, batched GEMM	Yes
cuSOLVERDx	LAPACK (Cholesky / TRSM / posv / …)	No — links a precompiled device fatbin

Download#

Go to https://developer.nvidia.com/cublasdx-downloads (a free NVIDIA Developer account is required).
Choose MathDx for CUDA 12, Linux x86_64 (.tar.gz). Version 25.12.x or later is recommended — that is the version the GLASS wrappers are tested against.

Install#

# Extract to /opt (or any directory you prefer)
tar -xzf MathDx_*.tar.gz -C /opt

# Confirm the version directory name
ls /opt/nvidia/mathdx/

# Set the environment variable (add to ~/.bashrc to persist)
export MATHDX_ROOT=/opt/nvidia/mathdx/25.12   # adjust version as needed

Verify#

ls $MATHDX_ROOT/include/cublasdx.hpp           # cuBLASDx header
ls $MATHDX_ROOT/include/cusolverdx.hpp         # cuSOLVERDx header
ls $MATHDX_ROOT/include/cusolverdx_io.hpp      # cuSOLVERDx IO helpers
ls $MATHDX_ROOT/lib/libcusolverdx.a            # cuSOLVERDx device library
echo $MATHDX_ROOT

Linking notes#

cuBLASDx is header-only — #include <cublasdx.hpp> is enough; no extra link flags.

cuSOLVERDx is not header-only: part of the implementation ships as a precompiled device fatbin (libcusolverdx.a). Any .cu file that uses cuSOLVERDx must add:

-rdc=true                      # relocatable device code (required for device link)
-dlto                          # link-time optimization (the fatbin is LTO-compiled)
-L$MATHDX_ROOT/lib
-lcusolverdx                   # the device library
-lcublas -lcusolver -lcudart   # host-side CUDA libs

The benchmark driver bench/run_bench.py adds these automatically when cusolverdx.hpp is present.

Required nvcc flags and known issues#

cuBLASDx headers require --expt-relaxed-constexpr (otherwise many #20013-D constexpr/__host__/__device__ warnings fire).
CUDA 12.9 cuBLASDx miscompilation: apply -Xptxas -O1 (this flag also doubles as anti-DSE for bench loops). If results are still wrong, add -DCUBLASDX_IGNORE_NVBUG_5218000_ASSERT.
cuSOLVERDx NVBUG 5288270 affects SM 1200 (Blackwell consumer) for some real-precision configs on CUDA ≤ 13.0. Define CUSOLVERDX_IGNORE_NVBUG_5288270_ASSERT to silence the static asserts, but verify correctness on your target arch first.

Next steps#

Library Overview — the single-block model and the call surfaces.
Quickstart — a minimal end-to-end kernel.
Using the NVIDIA Backend — a full compile-and-call walkthrough for the MathDx backend.

See Using the NVIDIA Backend for a full compile-and-call walkthrough.