Environment Variables

This page lists all environment variables that can modify dtFFT behavior at runtime, providing users with granular control over logging, performance measurement, and data transposition strategies.

Most of these variables override settings specified in the dtfft_config_t structure, allowing users to adjust configurations without modifying code.

DTFFT_ENABLE_LOG

Enables logging within the dtFFT library. By default, the library runs silently with no output. When enabled, it provides detailed insights into internal processes, aiding analysis and debugging.

Purpose

Logging enables monitoring of key operations, including:

  • Execution Times During Autotune: Logs timing data for autotuning stages.

Accepted Values

  • Type: Integer

  • Supported Values::

    • 0 (disabled)

    • 1 (enabled)

  • Default: 0

DTFFT_MEASURE_WARMUP_ITERS

Defines the number of warmup iterations performed when effort exceeds DTFFT_ESTIMATE. Warmup iterations ensure stable performance measurements in parallel environments.

Purpose

Warmup iterations stabilize system performance by preloading caches, establishing communication channels, and mitigating initial overhead. This is crucial for accurate benchmarking, especially in distributed setups, preventing skewed results from cold starts.

Accepted Values

  • Type: Non-negative integer

  • Recommended Range: 2–10 (values > 0 are advised for reliable results)

  • Default: 2

DTFFT_MEASURE_ITERS

Specifies the number of measurement iterations for transposition and data exchange when effort exceeds DTFFT_ESTIMATE. Multiple iterations enhance measurement reliability.

Purpose

Single measurements may be inconsistent due to system noise or cache effects. By repeating transpositions, dtFFT averages performance data, ensuring robust selection of optimal backends or kernel during autotuning.

Accepted Values

  • Type: Positive integer

  • Recommended Range: 5–20 (values > 1 balance accuracy and runtime)

  • Default: 5

DTFFT_PLATFORM

Specifies the execution platform for dtFFT plans. This environment variable allows users to override the platform set via the dtfft_config_t structure, taking precedence over API configuration.

Purpose

The DTFFT_PLATFORM variable provides a flexible way to control whether dtFFT executes on the host (CPU) or a CUDA-enabled GPU without modifying code or API calls. It ensures that runtime platform selection aligns with user preferences or system capabilities, prioritizing environment settings over programmatic defaults.

Accepted Values

  • Type: String

  • Supported Values:

    • host: Execute on the host (CPU).

    • cuda: Execute on a CUDA device (GPU).

  • Default: host

Note

  • Case-insensitive (e.g., HOST is equivalent to host).

  • Only applicable in builds with CUDA support (DTFFT_WITH_CUDA defined). In non-CUDA builds, it is ignored, and execution defaults to the host.

  • If an unsupported value is provided, it is silently ignored, and the default (host) is used.

DTFFT_BACKEND

Specifies the backend used by dtFFT for data transposition and communication when executing plans. This environment variable allows users to override the backend selected through the dtfft_config_t structure, taking precedence over API configuration.

Purpose

The DTFFT_BACKEND variable enables users to select a specific backend for optimizing data movement and computation in dtFFT plans. Different backends offer varying performance characteristics depending on the system configuration, workload, and MPI implementation, allowing fine-tuned control over execution without modifying code.

Accepted Values

  • Type: String

  • Supported Values:

    • mpi_dt: Backend using MPI datatypes.

    • mpi_p2p: MPI peer-to-peer backend.

    • mpi_a2a: MPI backend using MPI_Alltoallv.

    • mpi_p2p_pipe: Pipelined MPI peer-to-peer backend with overlapping data transfers and unpacking.

    • mpi_p2p_sched: MPI peer-to-peer backend with scheduled communication.

    • mpi_p2p_fused: Fused MPI peer-to-peer backend that integrates packing into the communication pipeline.

    • mpi_p2p_compressed: Extension of Fused MPI P2P backend that uses data compression before communication.

    • mpi_rma: MPI RMA backend that uses MPI_Rget for data transfers.

    • mpi_rma_pipe: Pipelined MPI RMA backend with overlapping data transfers and unpacking.

    • mpi_rma_fused: Fused MPI RMA backend that integrates packing into the communication pipeline.

    • mpi_rma_compressed: Extension of Fused MPI RMA backend that uses data compression before communication.

    • nccl: NCCL backend.

    • nccl_pipe: Pipelined NCCL backend with overlapping data transfers and unpacking.

    • nccl_compressed: NCCL backend that performs compression before data exchange and decompression after.

    • cufftmp: cuFFTMp backend.

    • cufftmp_pipe: cuFFTMp backend that uses additional buffer to avoid extra copy and gain performance.

    • adaptive: Adaptive backend that selects best backend for each transpose/reshape operation during plan creation.

  • Default: When built with CUDA Support: nccl if NCCL is available in the library build; otherwise, mpi_p2p. When built without CUDA Support: mpi_dt.

Note

  • Case-insensitive (e.g., MPI_DT is equivalent to mpi_dt).

  • If an unsupported value is provided, it is silently ignored, and the default backend (nccl or mpi_p2p, depending on build) is used.

  • Availability of some backends (e.g., nccl, cufftmp) depends on additional library support (e.g., NCCL, cuFFTMp) during compilation.

DTFFT_RESHAPE_BACKEND

Specifies the backend used by dtFFT specifically for data reshaping operations (converting between pencils and bricks). This environment variable allows users to override the reshape backend selected through the dtfft_config_t structure, taking precedence over API configuration.

Purpose

The DTFFT_RESHAPE_BACKEND variable enables users to independently control the backend used for reshape operations, which may have different performance characteristics than regular FFT transpositions. This allows fine-tuning of reshape performance without affecting the main FFT backend.

Accepted Values

  • Type: String

  • Supported Values: Same as DTFFT_BACKEND (see above)

  • Default: Same as DTFFT_BACKEND

Note

  • GPU Compatibility Requirement: When working on GPU (platform is cuda), the reshape backend must be compatible with the main backend’s communication library. Specifically:

    • If DTFFT_BACKEND uses NCCL (e.g., nccl or nccl_pipe), then DTFFT_RESHAPE_BACKEND must also use NCCL.

    • If DTFFT_BACKEND uses cuFFTMp (e.g., cufftmp or cufftmp_pipe), then DTFFT_RESHAPE_BACKEND must also use cuFFTMp.

    • MPI backends can be mixed with each other on GPU, but cannot be mixed with NCCL or cuFFTMp backends.

  • On CPU (platform is host), any backend combination is allowed.

DTFFT_NCCL_BUFFER_REGISTER

Specifies whether to enable buffer registration for NCCL operations. When enabled, NCCL buffers are registered, which can improve performance for certain workloads.

Purpose

Buffer registration can reduce the overhead of memory operations in NCCL by pre-registering memory regions. This is particularly useful for workloads with repeated communication patterns. However, in some cases, disabling registration may be beneficial, depending on the specific system configuration or workload characteristics.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable NCCL buffer registration.

    • 1: Enable NCCL buffer registration.

  • Default: 1

DTFFT_ENABLE_Z_SLAB

Specifies whether to enable Z-slab optimization for dtFFT plans. When enabled, Z-slab optimization reduces network data transfers by employing a two-dimensional FFT algorithm.

Purpose

Z-slab optimization is designed to improve performance for plans decomposed as NX × NY × NZ / P. Disabling it may resolve issues like DTFFT_ERROR_VKFFT_R2R_2D_PLAN or improve performance if the underlying 2D FFT implementation is suboptimal.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable Z-slab optimization.

    • 1: Enable Z-slab optimization.

  • Default: 1

DTFFT_ENABLE_Y_SLAB

Specifies whether to enable Y-slab optimization for dtFFT plans. When enabled, Y-slab optimization reduces network data transfers by employing a two-dimensional FFT algorithm.

Purpose

Y-slab optimization is designed to improve performance for plans decomposed as NX × NY / P × NZ. Disabling it may resolve issues like DTFFT_ERROR_VKFFT_R2R_2D_PLAN or improve performance if the underlying 2D FFT implementation is suboptimal.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable Y-slab optimization.

    • 1: Enable Y-slab optimization.

  • Default: 0

DTFFT_ENABLE_MPI_DT

Specifies whether to enable MPI datatype backend when effort is DTFFT_PATIENT. When enabled, the MPI datatype backend is tested during autotuning.

Purpose

The MPI datatype backend is a simple and robust method for data transposition using MPI derived datatypes. However, it may not be the most efficient option for large-scale systems or specific data layouts.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable MPI datatype backend.

    • 1: Enable MPI datatype backend.

  • Default: 1

DTFFT_ENABLE_MPI

Specifies whether to enable MPI-based backends for dtFFT when effort is DTFFT_PATIENT. When enabled, MPI backends (e.g., MPI P2P) are tested during autotuning.

Purpose

The following applies only to CUDA builds: MPI backends are useful for distributed GPU systems but may cause GPU memory leaks in certain OpenMPI versions. Disabling this option can prevent such issues.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable MPI-based backends.

    • 1: Enable MPI-based backends.

  • Default: 0

DTFFT_ENABLE_NCCL

Specifies whether to enable NCCL backends when effort is DTFFT_PATIENT. When enabled, NCCL backends are tested during autotuning.

Purpose

NCCL backends are optimized for GPU-to-GPU communication and can significantly improve performance in multi-GPU systems.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable NCCL backends.

    • 1: Enable NCCL backends.

  • Default: 1

Note

  • Only applicable in builds with CUDA support (DTFFT_WITH_CUDA defined) and when the execution platform is set to cuda (via DTFFT_PLATFORM or dtfft_config_t).

DTFFT_ENABLE_NVSHMEM

Specifies whether to enable NVSHMEM backends when effort is DTFFT_PATIENT. When enabled, NVSHMEM backends are tested during autotuning.

Purpose

NVSHMEM backends provide efficient communication for GPU clusters, leveraging shared memory capabilities.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable NVSHMEM backends.

    • 1: Enable NVSHMEM backends.

  • Default: 1

Note

  • Only applicable in builds with CUDA support (DTFFT_WITH_CUDA defined) and when the execution platform is set to cuda (via DTFFT_PLATFORM or dtfft_config_t).

DTFFT_ENABLE_PIPE

Specifies whether to enable pipelined backends when effort is DTFFT_PATIENT. When enabled, pipelined backends (e.g., overlapping data copy and unpack) are tested during autotuning.

Purpose

Pipelined backends improve performance by overlapping communication and computation, but they require additional internal buffers.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable pipelined backends.

    • 1: Enable pipelined backends.

  • Default: 1

DTFFT_ENABLE_RMA

Specifies whether to enable RMA backends when effort is DTFFT_PATIENT. When enabled, RMA backends are tested during autotuning.

Purpose

RMA backends provide efficient communication for distributed systems using Remote Memory Access.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable RMA backends.

    • 1: Enable RMA backends.

  • Default: 1

DTFFT_ENABLE_FUSED

Specifies whether to enable fused backends when effort is DTFFT_PATIENT. When enabled, fused backends are tested during autotuning.

Purpose

Fused backends improve the pipeline algorithm by integrating packing operations into the communication pipeline for enhanced performance. They utilize a round-robin communication schedule to optimize all-to-all data exchanges.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable fused backends.

    • 1: Enable fused backends.

  • Default: 1

DTFFT_ENABLE_COMPRESSED

Controls whether compressed backends are enabled during autotuning when effort is DTFFT_PATIENT or DTFFT_EXHAUSTIVE.

Purpose

Compressed backends use data compression techniques to reduce the amount of data transferred during transpositions, potentially improving performance for certain workloads. However, compression introduces additional computational overhead and may not always provide benefits.

Only fixed-rate compression can be used during autotuning, since it provides predictable performance characteristics and does not require data-dependent decisions at runtime. To enable compressed backends during autotuning, set this option to 1, set compression type to DTFFT_COMPRESSION_FIXED_RATE and provide desired compression rate.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable compressed backends during autotuning (default).

    • 1: Enable compressed backends during autotuning.

  • Default: 0

DTFFT_ENABLE_KERNEL_AUTOTUNE

Controls whether to enable kernel optimization when effort is below DTFFT_EXHAUSTIVE. When enabled, dtFFT tries to optimize kernel launch parameters during plan creation.

Purpose

Kernel optimization is always enabled for DTFFT_EXHAUSTIVE effort level. Setting this to 1 enables kernel optimization for lower effort levels (DTFFT_ESTIMATE, DTFFT_MEASURE, DTFFT_PATIENT). This may increase plan creation time but can improve runtime performance. Since kernel optimization is performed without data transfers, the time increase is usually minimal.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Disable kernel optimization for effort levels below DTFFT_EXHAUSTIVE.

    • 1: Enable kernel optimization for all effort levels.

  • Default: 0

Note

  • Only applicable in builds with CUDA support (DTFFT_WITH_CUDA defined) and when the execution platform is set to cuda (via DTFFT_PLATFORM or dtfft_config_t).

DTFFT_ENABLE_FOURIER_RESHAPE

Controls whether dtFFT should reshape data from pencils to bricks and vice versa in Fourier space during execute() calls.

Purpose

By default, dtFFT keeps data in pencil layout throughout the FFT process to minimize data transpositions and maximize performance. When this option is enabled, dtFFT performs additional reshaping operations to ensure data is in brick layout in Fourier space. This can be useful if you need to perform operations on the data in Fourier space between forward and backward transforms.

However, enabling this feature requires additional data transpositions, which will reduce overall performance. Only enable this option if your application specifically requires brick layout in Fourier space.

Accepted Values

  • Type: Integer

  • Accepted Values:

    • 0: Keep data in pencil layout in Fourier space (default, better performance).

    • 1: Reshape data to brick layout in Fourier space (reduced performance, but provides brick layout).

  • Default: 0

DTFFT_TRANSPOSE_MODE

Specifies at which stage the local transposition is performed during global exchange.

Purpose

By default, dtFFT performs local transposition before data exchange (packing) by executing single computationally intensive kernel. This is efficient for most cases, but in some scenarios, performing local transposition after data exchange (unpacking) may yield better performance. This variable allows users to select the preferred transpose mode based on their specific use case and system characteristics.

Accepted Values

  • Type: String

  • Supported Values:

    • pack: Perform local transposition before data exchange (default).

    • unpack: Perform local transposition after data exchange.

  • Default: pack

Note

DTFFT_ACCESS_MODE

Specifies the memory access pattern (write-aligned vs read-aligned) for local transposition in Generic backends.

Purpose

This variable controls the loop order during local data transposition. Write-aligned access (contiguous writes, scattered reads) is generally faster on CPUs due to better cache line utilization and avoiding false sharing in some cases. Read-aligned access (contiguous reads, scattered writes) might be beneficial on certain architectures or memory subsystems.

Accepted Values

  • Type: String

  • Supported Values:

    • write: Optimize for contiguous write access (default).

    • read: Optimize for contiguous read access.

  • Default: write

Note

This variable applies only to Host (CPU) execution.