# Performance Tuning¶

Note

For typical use cases, the default DALI configuration performs well out of the box, and you do not need to review this section.

This functionality allows you to pin DALI threads to the specified CPU. Thread affinity avoids the overhead of worker threads jumping from core to core and improves performance with CPU-heavy workloads. You can set the DALI CPU thread affinity by using the DALI_AFFINITY_MASK environment variable, which is a comma-separated list of CPU IDs that will be assigned to corresponding DALI threads. The number of DALI threads is set during the pipeline construction by the num_threads argument and set_affinity enables thread affinity for the CPU worker threads.

Note

For performance reasons, the hybrid nvidia.dali.fn.decoders.image() operator, which is nvJPEG based, creates threads on its own, and these threads are always affined.

In DALI_AFFINITY_MASK, if the number of threads is higher than the number of CPU IDs, the following process is applied:

1. The threads are assigned to the CPU IDs until all of the CPU IDs from DALI_AFFINITY_MASK are assigned.

2. For the remaining threads, the CPU IDs from nvmlDeviceGetCpuAffinity will be used.

An example:

num_threads=5


This example sets thread 0 to CPU 3, thread 1 to CPU 5, thread 2 to CPU 6, thread 3 to CPU 10, and thread 4 to the CPU ID that is returned by nvmlDeviceGetCpuAffinity.

## Memory Consumption¶

DALI uses the following memory types:

• Host

• Host-page-locked

• GPU

Allocating and freeing GPU and host page-locked (or pinned) memory require device synchronization. As a result, when possible, DALI avoids reallocating these kinds of memory. The buffers that are allocated with these storage types will only grow when the existing buffer is too small to accommodate the requested shape. This strategy reduces the number of total memory management operations and increases the processing speed when the memory requirements become stable and no more allocations are required.

By contrast, ordinary host memory is relatively inexpensive to allocate and free. To reduce host memory consumption, the buffers might shrink when the new requested size is smaller than the fraction of the old size. This is called shrink threshold. It can be adjusted to a value between 0 (never shrink) and 1 (always shrink). The default is 0.9. The value can be controlled by the DALI_HOST_BUFFER_SHRINK_THRESHOLD environmental variable or be set in Python by calling the nvidia.dali.backend.SetHostBufferShrinkThreshold function.

During processing, DALI works on batches of samples. For GPU and some CPU operators, each batch is stored as contiguous memory and is processed at once, which reduces the number of necessary allocations. For some CPU operators that cannot calculate their output size ahead of time, the batch is stored as a vector of separately allocated samples.

For example, if your batch consists of nine 480p images and one 4K image in random order, the contiguous allocation can accommodate all possible combinations of these batches. On the other hand, the CPU batch that is stored as separate buffers needs to keep a 4K allocation for every sample after several iterations. The GPU buffers that keep the operator outputs can grow are as large as the largest possible batch, whereas the non-contiguous CPU buffers can reach the size of the largest sample in the data set multiplied by the number of samples in the batch.

The host and the GPU buffers have a configurable growth factor. If the factor is greater than 1, and to potentially avoid subsequent reallocations. This functionality is disabled by default, and the growth factor is set to 1. The growth factors can be controlled with the DALI_HOST_BUFFER_GROWTH_FACTOR and DALI_DEVICE_BUFFER_GROWTH_FACTOR environmental variables and with the nvidia.dali.backend.SetHostBufferGrowthFactor and nvidia.dali.backend.SetDeviceBufferGrowthFactor Python API functions. For convenience, the DALI_BUFFER_GROWTH_FACTOR environment variable and the nvidia.dali.backend.SetBufferGrowthFactor Python function can be used to set the same growth factor for the host and the GPU buffers.

## Operator Buffer Presizing¶

When you can precisely forecast the memory consumption during a DALI run, this functionality helps you fine tune the processing pipeline. One of the benefits is that the overhead of some reallocations can be avoided.

DALI uses intermediate buffers to pass data between operators in the processing graph. The capacity of this buffers is increased to accommodate new data, but is never reduced. Sometimes, however, even this limited number of allocations might still affect DALI performance. If you know how much memory each operator buffer requires, you can add a hint to preallocate the buffers before the pipeline is first run.

The following parameters are available:

• The bytes_per_sample pipeline argument, which accepts one value that is used globally across all operators for all buffers.

• The bytes_per_sample_hint per operator argument, which accepts one value or a list of values.

When one value is provided, it is used for all output buffers for an operator. When a list is provided, each operator output buffer is presized to the corresponding size. To determine the amount of memory output that each operator needs, complete the following tasks:

1. Create the pipeline by setting enable_memory_stats to True.

2. Query the pipeline for the operator’s output memory statistics by calling the nvidia.dali.Pipeline.executor_statistics() method on the pipeline.

The max_real_memory_size value represents the biggest tensor in the batch for the outputs that allocate memory per sample and not for the entire batch at the time or the average tensor size when the allocation is contiguous. This value should be provided to bytes_per_sample_hint.

## Prefetching Queue Depth¶

The DALI pipeline allows the buffering of one or more batches of data, which is important when the processing time varies from batch to batch. The default prefetch depth is 2. You can change this value by using the prefetch_queue_depth pipeline argument. If the variation is not hidden by the default prefetch depth value, we recommend that you prefetch more data ahead of time.

Note

Increasing queue depth also increases memory consumption.