nvprof

nvprof
Name	nvprof
Developer	NVIDIA
Released	2013
Latest release	8.x (legacy)
Operating system	Linux, Windows
Genre	Performance profiler
License	Proprietary

nvprof nvprof is a legacy command-line profiler developed by NVIDIA for performance analysis of CUDA applications, designed to collect hardware and software metrics, trace kernels, and measure memory transfers. It integrates with NVIDIA tools and platforms to assist developers targeting GPUs in optimizing kernels, interacting with drivers, and correlating results with system behavior. nvprof was widely used across HPC centers, research labs, and technology firms before being succeeded by newer tooling from NVIDIA.

Overview

nvprof provided low-overhead collection of CUDA runtime and driver API activity, kernel execution timelines, and hardware performance counters for NVIDIA GPU architectures such as Pascal architecture, Volta, Turing, and Ampere. It interoperated with ecosystems including CUDA Toolkit, Nsight Compute, Nsight Systems, and platform vendors like Red Hat, Ubuntu, Microsoft Windows, and CentOS. Development teams at organizations like NVIDIA Research, Lawrence Berkeley National Laboratory, Argonne National Laboratory, Oak Ridge National Laboratory, and companies such as Google, Facebook, Amazon Web Services, and Intel research groups used nvprof in profiling pipelines. nvprof enabled correlations between application-level events and GPU-level counters used in performance engineering workflows influenced by methodologies from institutions such as ACM and IEEE.

Installation and Compatibility

nvprof was distributed with certain versions of the CUDA Toolkit and required compatible NVIDIA driver stacks and supported GPU architectures. Installation typically followed instructions for distributions like Ubuntu, Debian, Red Hat Enterprise Linux, or Microsoft Windows Server and depended on matching versions of CUDA Toolkit and drivers from NVIDIA. System administrators at centers such as National Energy Research Scientific Computing Center and Oak Ridge Leadership Computing Facility managed dependencies including kernel modules and package managers like apt and yum. Compatibility matrices referenced specific toolchains including compilers from GCC, Clang, and integrations with build systems like CMake and Bazel.

Usage and Command Options

nvprof operated as a wrapper around application execution, invoked from shells such as Bash, Zsh, or PowerShell, and supported options to enable event sampling, metric collection, and output formatting. Common flags allowed collection of counters like achieved occupancy, memory throughput, and warp execution efficiency, and options to filter by CUDA stream or kernel name. Output modes produced summaries consumable by postprocessing tools from NVIDIA, or by researchers using analysis frameworks developed at organizations like MIT, Stanford University, University of California, Berkeley, and Princeton University. Integration points included build and CI pipelines at companies including NVIDIA, IBM, AMD, and cloud platforms such as Google Cloud Platform and Amazon EC2.

Profiling Metrics and Output

nvprof collected metrics derived from GPU hardware performance counters and software-level events: kernel duration, occupancy, achieved occupancy, warp serialization, memory throughput, global load/store transactions, L1/L2 cache hit rates, shared memory utilization, and PCIe transfer timings. Results were emitted as textual summaries, CSV, or intermediate formats used by visualization tools such as Nsight Compute and Nsight Systems. Performance engineering groups from institutions including Lawrence Livermore National Laboratory, European Organization for Nuclear Research, Fermilab, and NASA used nvprof outputs to drive optimization efforts. Metrics referenced microarchitectural features present in architectures like Maxwell and Kepler.

Examples and Common Workflows

Typical nvprof workflows instrumented iterative kernel launches, measured host-to-device transfers, and compared baseline and optimized kernels across runs. Developers from projects at Los Alamos National Laboratory, Sandia National Laboratories, CERN, Facebook AI Research, and academic groups at Harvard University used nvprof traces to identify bottlenecks such as uncoalesced memory accesses or low occupancy. Common patterns included collecting timeline traces for visualization alongside debugger sessions with cuda-gdb or inspection via integrated development environments maintained by NVIDIA and partners like JetBrains and Eclipse Foundation-based tools. Teams often combined nvprof data with roofline analyses and tools developed in consortia like TOP500 and SC Conference proceedings.

Limitations and Deprecation

nvprof had limitations: per-thread granularity constraints, overhead on short-running kernels, limited support for multi-process or containerized environments, and evolving metric availability across GPU generations. NVIDIA announced deprecation in favor of newer tools, and guidance from NVIDIA engineering advocated migration to successor tooling. Research groups and enterprise teams transitioned profiling workflows in response to feature gaps and maintenance considerations discussed in venues such as GTC and publications in ACM SIGARCH.

Alternatives and Successors

Successors include Nsight Compute, Nsight Systems, and integrations with vendor and open-source profilers and frameworks from organizations including Intel Corporation, AMD, Google, and projects incubated by Linux Foundation and OpenACC-related communities. Other performance tools used in the ecosystem include perf (Linux), VTune, Arm MAP, and domain-specific profilers adopted by teams at NVIDIA Deep Learning Institute, OpenAI, DeepMind, and major research labs. Migration paths recommended by practitioners at institutions like Lawrence Berkeley National Laboratory and companies such as NVIDIA involve using Nsight tools for metric collection, timeline analysis, and support for modern GPU architectures.

Category:Profilers