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About 10,000 papers for “CUDA”
Buddhi Ashan · Zenodo (CERN European Organization for Nuclear Research) · 2023
Features Individual test cases can be executed using bash files Debug mode is added to see more information in the terminal Bug Fix Waiting at the start of the program bug fixed <strong>Full Changelog</strong>: https://github.com/buddhi1/GH-CUDA/compare/v1.1.2...v1.1.3
Cited by 0Open access
Porter, Matthew A. · Zenodo (CERN European Organization for Nuclear Research) · 2025
Qompass AI on CUDA
Cited by 0Open access
Porter, Matthew A. · Zenodo (CERN European Organization for Nuclear Research) · 2025
Qompass AI on CUDA
Cited by 0Open access
Massimiliano Fatica, Gregory Ruetsch · Elsevier eBooks · 2013
Cited by 0
Tourani, Ali · 2018
Cited by 0Open access
Yi Yang, Huiyang Zhou · ACM SIGPLAN Notices · 2014
Parallel programs consist of series of code sections with different thread-level parallelism (TLP). As a result, it is rather common that a thread in a parallel program, such as a GPU kernel in CUDA programs, still contains both se-quential code and parallel loops. In order to leverage such parallel loops, the latest Nvidia Kepler architecture intro-duces dynamic parallelism, which allows a GPU thread to start another GPU kernel, thereby reducing the overhead of launching kernels from a CPU. However, with dynamic parallelism, a parent thread can only communicate with its child threads through global memory and the overhead of launching GPU kernels is non-trivial even within GPUs. In this paper, we first study a set of GPGPU benchmarks that contain parallel loops, and highlight that these bench-marks do not have a very high loop count or high degrees of TLP. Consequently, the benefits of leveraging such par-allel loops using dynamic parallelism are too limited to offset its overhead. We then present our proposed solution to exploit nested parallelism in CUDA, referred to as CUDA-NP. With CUDA-NP, we initially enable a high number of threads when a GPU program starts, and use control flow to activate different numbers of threads for different code sections. We implemented our proposed CUDA-NP framework using a directive-based compiler approach. For a GPU kernel, an application developer only needs to add OpenMP-like pragmas for parallelizable code sections. Then, our CUDA-NP compiler automatically gen-erates the optimized GPU kernels. It supports both the reduction and the scan primitives, explores different ways to distribute parallel loop iterations into threads, and effi-ciently manages on-chip resource. Our experiments show that for a set of GPGPU benchmarks, which have already been optimized and contain nested parallelism, our pro-posed CUDA-NP framework further improves the perfor-mance by up to 6.69 times and 2.18 times on average.
Cited by 12
Team, The CUDA Quantum Development · Zenodo (CERN European Organization for Nuclear Research) · 2023
The 0.4.1 release adds support for ARM processors in the form of multi-platform Docker images and aarch64 Python wheels. Additionally, all GPU-based backends are now included in the Python wheels as well as in the Docker image. <!-- Release notes generated using configuration in .github/release.yml at 2694e6828368154e0b1ac916f9ecaa4d2f3ec7ee --> What's Changed Features and Enhancements 🎉 Expose adjoint T and S gates to python builder API by @anthony-santana in https://github.com/NVIDIA/cuda-quantum/pull/402 Added kernel_builder functions for the hardware efficient ansatz #361 by @johanneskuhlmann in https://github.com/NVIDIA/cuda-quantum/pull/451 Adding nvidia backends to python wheels by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/480 Multi-platform Docker images and aarch64 wheels by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/548 Implement CUDA Quantum Kernel from State Vector. by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/568 Enable std::vector<T> construction within kernels by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/593 ### Bug Fixes 🐛 Support Volta+ arch in custatevec backend by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/696 Accurately detect mx/my mid-circuit measurements by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/415 Bug fix to support multiple single qubit allocations on quantum backends by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/474 Fix missing support for R1 decomposition by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/471 Fix #475: bug with measurement operations in value semantics by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/491 Fix #303 - release build segfault by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/502 Fixes bug with null_wire. by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/504 Fix bug in bit flip noise channel by @anthony-santana in https://github.com/NVIDIA/cuda-quantum/pull/528 [Support] Closes #532: change backtrace error message by @boschmitt in https://github.com/NVIDIA/cuda-quantum/pull/534 Fix #523 and std::unique bug in CircuitSimulator by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/526 Fix compiler crash when processing empty region by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/552 Bug fixes for <code>nvq++</code> arguments by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/549 Add spin_op::from_word(...) by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/555 Add multi-control decomposition to IONQ pipeline by @boschmitt in https://github.com/NVIDIA/cuda-quantum/pull/566 Support QASM emission for CallOps by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/563 Convert IonQ outputs to bit strings and correct order by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/575 Canonicalizations for ConcatOp and ExtractRefOp by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/574 [custatevec] <code>getStateData</code> to return data consistent with qpp and <code>cudaq::from_state</code> by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/582 Allow more descriptive output names by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/580 Fix for issue #603: QASM emission by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/604 Fix bug when using gcc-11 header files. by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/613 Fix issues with get_state() in Python by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/616 [Opt] Fix use after free (erase) in mem2reg by @boschmitt in https://github.com/NVIDIA/cuda-quantum/pull/628 Fix for issue #641: QASM export by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/649 Only enable optimization in non-empty kernels by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/656 Add the ability to pass additional command line options by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/647 ### Documentation Updates ✏️ Clarifying documentation for GPU-based simulators by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/738 Document CUDA library dependencies for Python wheels by @anthony-santana in https://github.com/NVIDIA/cuda-quantum/pull/716 Updating docs on built-in operations by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/719 Document GPU and CPU requirements by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/707 Adding mqpu documentation by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/708 Fixed code snippets using wrong kernel_builder APIs by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/479 Cleanup the MLIR Pass advanced document, add a FileCheck test by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/547 Refactor python documentation and update examples by @anthony-santana in https://github.com/NVIDIA/cuda-quantum/pull/581 Adding tutorials folder with example notebooks by @zohimchandani in https://github.com/NVIDIA/cuda-quantum/pull/560 ### Other Changes Update unwind-lowering pass to be more general by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/389 Improve synthesis. by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/401 updating cuquantum and cutensor versions by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/422 Add environment variable allowing one to print the IR before remote submission by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/427 Issue #274: Migrate the fixup-linkage script to C++ by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/497 Add cudaq::set_random_seed() by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/477 Python sample() and observe() implement broadcasting by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/495 Add option to set the noise model when using the IonQ-hosted simulator by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/536 Add all_gather function for MPI by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/516 Enable one to progressively build a Kernel with the kernel_builder by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/558 Implement spin_op::to_sparse_matrix(). by @amccaskey in https://github.com/NVIDIA/cuda-quantum/pull/567 Rewrite the AST Bridge. by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/587 Adjustments to support libc++ in the AST Bridge by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/594 Improve reproducibility of results by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/617 More precise representation of the dataflow in Quake value semantics by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/625 Add get_random_seed() to make broadcast tests repeatable by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/648 Modify Quake::ApplyOp to allow an indirect form of call in order to by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/663 Improve measurement support for submissions to IonQ by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/661 Add ctrl-t and ctrl-s decomposition via r1 conversion by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/672 Introduce sample_options and observe_options for addl flexibility by @bmhowe23 in https://github.com/NVIDIA/cuda-quantum/pull/664 Fix #633: Bump the LLVM version to 16.0.6. by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/676 New Contributors @orclassiq made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/394 @johanneskuhlmann made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/451 @jrhemstad made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/447 @kukushechkin made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/468 @ajschmidt8 made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/585 @jfriel-oqc made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/435 @zohimchandani made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/560 <strong>Full Changelog</strong>: https://github.com/NVIDIA/cuda-quantum/compare/0.4.0...6408359768 Release created by workflow 6408359768.
Cited by 0Open access
Kim, Keon Hee · Zenodo (CERN European Organization for Nuclear Research) · 2025
MBTRACK2-CUDA is a CUDA ported version of MBTRACK2. MBTRACK2-CUDA supports GPU parallel computing using Python with Numba-CUDA.
Cited by 0Open access
Bu Bror, Mahmut Cda'nn, ubat -9 Marl et al. · Dspace Repository (Marmara Üniversitesi) · 2018
Taha Toros Arşivi, Dosya No: 142-Mahmut Cuda. \nNot: Sergi, 24 Şubat - 9 Mart 1976 tarihleri arasında düzenlenmiştir.
Cited by 0Open access
Langdon, WB · UCL Discovery (University College London) · 2011
During six months of intensive nVidia CUDA C programming many bugs were created. We pass on the software engineering lessons learnt, particularly those relevant to parallel general-purpose computation on graphics hardware GPGPU. © 2011 ACM.
Cited by 0
Tolga Soyata · 2018
In a Compute-Unified Device Architecture program, first the data must be transferred from the CPU memory into the Graphics Processing Unit (GPU) memory; it is only when the data is in GPU memory that the GPU cores can access it and process it. When the kernel execution is done, the processed data must be transferred back into the CPU memory. A virtual address is only an "illusion" and cannot be used to access any data unless it is translated into a physical address; a physical address is an actual address that is used to access the data in dynamic random access memory main memory. Nvidia GPUs need two different types of engines to implement streaming: kernel execution engine, and copy engine. The purpose of the copy engine is to queue the incoming and outgoing operations and perform them when the Peripheral Component Interconnect express bus is available.
Cited by 0
Yi Yang, Huiyang Zhou · 2014
Parallel programs consist of series of code sections with different thread-level parallelism (TLP). As a result, it is rather common that a thread in a parallel program, such as a GPU kernel in CUDA programs, still contains both se-quential code and parallel loops. In order to leverage such parallel loops, the latest Nvidia Kepler architecture intro-duces dynamic parallelism, which allows a GPU thread to start another GPU kernel, thereby reducing the overhead of launching kernels from a CPU. However, with dynamic parallelism, a parent thread can only communicate with its child threads through global memory and the overhead of launching GPU kernels is non-trivial even within GPUs. In this paper, we first study a set of GPGPU benchmarks that contain parallel loops, and highlight that these bench-marks do not have a very high loop count or high degrees of TLP. Consequently, the benefits of leveraging such par-allel loops using dynamic parallelism are too limited to offset its overhead. We then present our proposed solution to exploit nested parallelism in CUDA, referred to as CUDA-NP. With CUDA-NP, we initially enable a high number of threads when a GPU program starts, and use control flow to activate different numbers of threads for different code sections. We implemented our proposed CUDA-NP framework using a directive-based compiler approach. For a GPU kernel, an application developer only needs to add OpenMP-like pragmas for parallelizable code sections. Then, our CUDA-NP compiler automatically gen-erates the optimized GPU kernels. It supports both the reduction and the scan primitives, explores different ways to distribute parallel loop iterations into threads, and effi-ciently manages on-chip resource. Our experiments show that for a set of GPGPU benchmarks, which have already been optimized and contain nested parallelism, our pro-posed CUDA-NP framework further improves the perfor-mance by up to 6.69 times and 2.18 times on average.
Cited by 63
Istanbul Şehir University Institutional Repository (Istanbul Şehir University) · 1976
Taha Toros Arşivi, Dosya No: 142-Mahmut Cuda. \nNot: Sergi, 24 Şubat - 9 Mart 1976 tarihleri arasında düzenlenmiştir.
Cited by 0Open access
The CUDA-Q development team · Open MIND · 2026
This release comes with a range of exciting improvements. This release includes the first 0.1.0 version of CUDA-Q Realtime. CUDA-Q Realtime is built to support low-latency high-bandwidth data transfer between FPGAs and GPUs using NVQLink. CUDA-Q Realtime is installed separately via the C++ installer provided in the release assets. For more information about CUDA-Q Realtime, please see our docs. 0.14.0 adds support for Pre-Trajectory Sampling with Batch Execution (PTSBE). PTSBE can be used to capture millions of times more noisy shot data, which can then be used as training data in ML tasks such as AI decoders, or it can be deployed proportionally, capturing the exact statistics of the problem while still offering a considerable speedup. For more information, see our docs and applications. Starting with version 0.14.0, we now also distribute MacOS wheels on PyPI, and have made the C++ installer usable without root privileges. Additionally, we have completely overhauled the Python compiler. More details about the changes can be found in https://github.com/NVIDIA/cuda-quantum/pull/3537 and https://github.com/NVIDIA/cuda-quantum/pull/3693. What's Changed Features and Enhancements 🎉 Introducing CUDA-Q Realtime by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/4118 Complete revision of the way CUDA-Q embeds in Python by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3693 Add MacOS support by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3840 Pre-Trajectory Sampling with Batch Execution by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3995 Make CUDA-Q installer non-root installable by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3993 Scaleway integration by @vmscw in https://github.com/NVIDIA/cuda-quantum/pull/3781 Add support for TII backend by @GabrielePalazzo in https://github.com/NVIDIA/cuda-quantum/pull/3797 Introduces Pasqal CUDA-Q QRMI integration by @awennersteen in https://github.com/NVIDIA/cuda-quantum/pull/4023 Bug Fixes 🐛 Fix some bugs in evolve_async related to storing intermediate results by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3636 Fix an issue during lowering of callables defined within kernels by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3649 Support for zsh in cudaq_set_env.sh by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3592 Fix an issue for expectation value computations with noise on the tensornet-mps backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3709 Fix issue during migration to host in tensornet backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3713 Fix Runge-Kutta second order midpoint formula by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3720 Fix issue in batched execution of dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3725 Fix a bug in overlap computation for tensornet-mps backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3749 Fix density matrix indexing bug for dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3768 Parameter fix for exp_pauli decomposition by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3775 Fix segmentation fault in distributed batched state evolution with store_intermediate_results by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3771 Handling distributed batched results for dynamics backend by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3794 Fix issue with uninitialized values for noise channels by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3811 Fix column-major indexing in dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3836 Fix list[pauli_word] broadcast detection in sample/observe by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3861 Fix cudaq.control(...) type specialization bug by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3860 Bug fix for using run on emulated targets by @atgeller in https://github.com/NVIDIA/cuda-quantum/pull/3868 Fix dynamics backend to accept density-matrix initial state by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3967 Fix density matrix overlap dimension by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3909 Fix sample_async noise model lifetime and isolation by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3857 Captured args are resolved in kernel definition scope by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/4005 Fix cmake bug related to interop by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/4006 Fix bug in exp_pauli qubit concatenation by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4064 Fix segfault in EvolveResult.final_expectation_values() when no observables were provided by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4065 Fix a potential segfault in conda environment by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4066 Fix integer overflow for 31+ qubits by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4093 Bug fix for using cudaq.translate with kernel builder by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4105 Fix potential seg fault in evolve_async by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4114 Breaking Changes 🛠 Python language translation revision by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/3537 Removing NVQC target and associated functions by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3633 Remove C++17 support from CUDA-Q runtime libraries by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3690 Remove deprecated platform-wide shot count by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3701 Disallow kernels with conditional branching on measurement results by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3731 Fixing depolarization2 channel by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3846 Bump cuquantum version by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3866 Documentation Updates ✏️ Updated SKQD tutorial by @zohimchandani in https://github.com/NVIDIA/cuda-quantum/pull/3643 Fixing an incorrect weight in QAOA notebook by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3654 Update documentation on run return types and supported targets by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3692 Update docs and usage examples for optimizers by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3822 Migration guide for using run instead of sample by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/4070 Fix divisive clustering notebook by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4121 Update tutorial unitary_compilation_diffusion_models by @FlorianFuerrutter in https://github.com/NVIDIA/cuda-quantum/pull/4123 Other Changes Expose resource estimates as dictionary by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3662 Add Decomposition Pattern Selection logic by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3644 Remove unnecessary restriction during evolution of custom operators by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3712 Reduce data type conversion overhead in Torch integrator implementation by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3779 Python/C++ interoperability tools by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3791 Support multi-term SpinOperators in Observe by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3835 Fix perf issue with adaptive solvers by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3844 Add two-qubit Pauli product gate decomposition, id gate and fix state reset by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3935 Support creating qubit lists in Python list comprehension by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4031 Defer Python kernel compilation until invocation by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3948 New Contributors @efratshabtai made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3672 @AndrewTKent made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3739 @dependabot[bot] made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3903 @derek-yux made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3984 @vmscw made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3781 @jeis4wpi made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/4076 @GabrielePalazzo made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3797 @QuantumRaul made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3847 Full Changelog: https://github.com/NVIDIA/cuda-quantum/compare/0.13.0...0.14.0 Release created by workflow 23132024839.
Cited by 0Open access
Istanbul Şehir University Institutional Repository (Istanbul Şehir University) · 2018
Cited by 0Open access
Timothy Masters · Apress eBooks · 2018
The source code that can be downloaded for free from my web site contains four large source files that handle the vast majority of the computation involved in propagating activations and backpropagating deltas for all layer types involved in convolutional nets.
Cited by 0
Greg Ruetsch, Massimiliano Fatica · Elsevier eBooks · 2017
Cited by 0
William B. Langdon, Ronny Lorenz · bioRxiv (Cold Spring Harbor Laboratory) · 2018
Abstract We add CUDA GPU C program code to RNAfold to enable both it to be run on nVidia gaming graphics hardware and so that many thousands of RNA secondary structures can be computed in parallel. RNAfold predicts the folding pattern for RNA molecules by using O( n 3 ) dynamic programming matrices to minimise the free energy of treating them as a sequence of bases. We benchmark RNAfold on RNA STRAND and artificial sequences of upto 30 000 bases on two GPUs and a GPGPU Tesla. The speed up is variable but up to 14 times.
Cited by 1Open access
Gabriela Woźniak-Kowalik · RCIN (Digital Repository of the Scientifics Institutes) (Institute of Archaeology and Ethnology of the Polish Academy of Sciences)
dwujęzyczna gazeta: teksty ukazują się po polsku i po ukraińsku
Cited by 0
The CUDA-Q development team · Open MIND · 2026
This release comes with a range of exciting improvements. This release includes the first 0.1.0 version of CUDA-Q Realtime. CUDA-Q Realtime is built to support low-latency high-bandwidth data transfer between FPGAs and GPUs using NVQLink. CUDA-Q Realtime is installed separately via the C++ installer provided in the release assets. For more information about CUDA-Q Realtime, please see our docs. 0.14.0 adds support for Pre-Trajectory Sampling with Batch Execution (PTSBE). PTSBE can be used to capture millions of times more noisy shot data, which can then be used as training data in ML tasks such as AI decoders, or it can be deployed proportionally, capturing the exact statistics of the problem while still offering a considerable speedup. For more information, see our docs and applications. Starting with version 0.14.0, we now also distribute MacOS wheels on PyPI, and have made the C++ installer usable without root privileges. Additionally, we have completely overhauled the Python compiler. More details about the changes can be found in https://github.com/NVIDIA/cuda-quantum/pull/3537 and https://github.com/NVIDIA/cuda-quantum/pull/3693. What's Changed Features and Enhancements 🎉 Introducing CUDA-Q Realtime by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/4118 Complete revision of the way CUDA-Q embeds in Python by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3693 Add MacOS support by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3840 Pre-Trajectory Sampling with Batch Execution by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3995 Make CUDA-Q installer non-root installable by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3993 Scaleway integration by @vmscw in https://github.com/NVIDIA/cuda-quantum/pull/3781 Add support for TII backend by @GabrielePalazzo in https://github.com/NVIDIA/cuda-quantum/pull/3797 Introduces Pasqal CUDA-Q QRMI integration by @awennersteen in https://github.com/NVIDIA/cuda-quantum/pull/4023 Bug Fixes 🐛 Fix some bugs in evolve_async related to storing intermediate results by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3636 Fix an issue during lowering of callables defined within kernels by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3649 Support for zsh in cudaq_set_env.sh by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3592 Fix an issue for expectation value computations with noise on the tensornet-mps backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3709 Fix issue during migration to host in tensornet backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3713 Fix Runge-Kutta second order midpoint formula by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3720 Fix issue in batched execution of dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3725 Fix a bug in overlap computation for tensornet-mps backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3749 Fix density matrix indexing bug for dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3768 Parameter fix for exp_pauli decomposition by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3775 Fix segmentation fault in distributed batched state evolution with store_intermediate_results by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3771 Handling distributed batched results for dynamics backend by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3794 Fix issue with uninitialized values for noise channels by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3811 Fix column-major indexing in dynamics backend by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3836 Fix list[pauli_word] broadcast detection in sample/observe by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3861 Fix cudaq.control(...) type specialization bug by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3860 Bug fix for using run on emulated targets by @atgeller in https://github.com/NVIDIA/cuda-quantum/pull/3868 Fix dynamics backend to accept density-matrix initial state by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3967 Fix density matrix overlap dimension by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3909 Fix sample_async noise model lifetime and isolation by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/3857 Captured args are resolved in kernel definition scope by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/4005 Fix cmake bug related to interop by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/4006 Fix bug in exp_pauli qubit concatenation by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4064 Fix segfault in EvolveResult.final_expectation_values() when no observables were provided by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4065 Fix a potential segfault in conda environment by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4066 Fix integer overflow for 31+ qubits by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4093 Bug fix for using cudaq.translate with kernel builder by @huaweil-nv in https://github.com/NVIDIA/cuda-quantum/pull/4105 Fix potential seg fault in evolve_async by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4114 Breaking Changes 🛠 Python language translation revision by @bettinaheim in https://github.com/NVIDIA/cuda-quantum/pull/3537 Removing NVQC target and associated functions by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3633 Remove C++17 support from CUDA-Q runtime libraries by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3690 Remove deprecated platform-wide shot count by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3701 Disallow kernels with conditional branching on measurement results by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3731 Fixing depolarization2 channel by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3846 Bump cuquantum version by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3866 Documentation Updates ✏️ Updated SKQD tutorial by @zohimchandani in https://github.com/NVIDIA/cuda-quantum/pull/3643 Fixing an incorrect weight in QAOA notebook by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3654 Update documentation on run return types and supported targets by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/3692 Update docs and usage examples for optimizers by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3822 Migration guide for using run instead of sample by @khalatepradnya in https://github.com/NVIDIA/cuda-quantum/pull/4070 Fix divisive clustering notebook by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4121 Update tutorial unitary_compilation_diffusion_models by @FlorianFuerrutter in https://github.com/NVIDIA/cuda-quantum/pull/4123 Other Changes Expose resource estimates as dictionary by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3662 Add Decomposition Pattern Selection logic by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3644 Remove unnecessary restriction during evolution of custom operators by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3712 Reduce data type conversion overhead in Torch integrator implementation by @1tnguyen in https://github.com/NVIDIA/cuda-quantum/pull/3779 Python/C++ interoperability tools by @schweitzpgi in https://github.com/NVIDIA/cuda-quantum/pull/3791 Support multi-term SpinOperators in Observe by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3835 Fix perf issue with adaptive solvers by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/3844 Add two-qubit Pauli product gate decomposition, id gate and fix state reset by @taalexander in https://github.com/NVIDIA/cuda-quantum/pull/3935 Support creating qubit lists in Python list comprehension by @sacpis in https://github.com/NVIDIA/cuda-quantum/pull/4031 Defer Python kernel compilation until invocation by @lmondada in https://github.com/NVIDIA/cuda-quantum/pull/3948 New Contributors @efratshabtai made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3672 @AndrewTKent made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3739 @dependabot[bot] made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3903 @derek-yux made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3984 @vmscw made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3781 @jeis4wpi made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/4076 @GabrielePalazzo made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3797 @QuantumRaul made their first contribution in https://github.com/NVIDIA/cuda-quantum/pull/3847 Full Changelog: https://github.com/NVIDIA/cuda-quantum/compare/0.13.0...0.14.0 Release created by workflow 23132024839.
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