Performance tuning

While spead2 tries to be performant out of the box, there are a number of ways one can tune both the system and the application using spead2. It is usually necessary to do at least some of these steps to achieve performance of 10Gb/s+, but your mileage may vary depending on your hardware and application.

This guide focuses mostly on the problem of receiving data, because my experience with high-bandwidth SPEAD has been with data produced by FPGAs. Nevertheless, some of these tips also apply to sending data.

All advice is for a GNU/Linux system with an x86-64 CPU. You will need to consult other documentation to find equivalent commands for other systems.

System tuning

Kernel bypass networking

For the best performance it is necessary to bypass the kernel networking stack. At present the only kernel bypass technology supported by spead2 is ibverbs. Refer to that documentation for setup and tuning instructions. Note that at present this is only known to work with NVIDIA NICs.

Kernel network stack

If you’re unable to bypass the kernel networking stack, this section has some advice on tuning it. The first thing to do is to increase the maximum socket buffer sizes. See Introduction to spead2 for details.

The kernel firewall can affect performance, particularly if small packets are being used (in this context, anything that isn’t a jumbo frame is considered “small”). If possible, remove all firewall rules and unload the kernel modules (those prefixed with ipt or nf). In particular, simply having the nf_conntrack module loaded can reduce performance by several percent.

IP fragmentation also causes performance problems on the receiver. Check that the routers in your network have a sufficiently large MTU that packets do not get fragmented, particularly if using jumbo frames. You can use tcpdump -v to see fragments.

The above all applies to UDP. For TCP, dropped packets are far less of a concern, and overly large buffers may actually be counter-productive as they do not fit in cache and lead to buffer-bloat. The simplest way to improve performance is to increase the packet size in the sender.


The constructors for the TCP and UDP stream classes take an interface_address argument, but the actual behaviour depends on the constructor used and the OS. For multicast constructors, it sets the IP_MULTICAST_IF socket option and hence directly determines the interface used. In other cases, it only determines the source IP address (via bind(2)), and the effect depends on the operating system. On Linux, the routing table determines the interface, so this setting will only impact routing if you have policy routing configured.


On a system with multiple CPU sockets, it is important to pin the process using spead2 to a single socket, so that memory accesses do not cross the inter-socket bus. For best performance, use the same socket as the NIC, which can be determined from the output of hwloc-ls. See numactl(8), hwloc-ls(1), hwloc-bind(1).

There are a number of settings that can be adjusted to improve the system’s ability to respond to bursts of data. These will probably not improve peak performance, but can reduce the number of lost heaps, particularly when a stream starts and the system must ramp up performance in response.

  • Disable hyperthreading.

  • Disable CPU frequency scaling.

  • Disable C states beyond C1 (for example, by passing processor.max_cstate=1 to the Linux kernel). Disabling C1 as well may reduce latency, but will likely limit the gains from Turbo Boost.

  • Investigate disabling the P-state driver by passing intel_pstate=disable on the kernel command line. The P-state driver has sometimes been reported to be much slower 1, 2, but can also be faster 3.

  • Disable adaptive interrupt moderation on the NIC: ethtool -C interface adaptive-rx off adaptive-tx off. You may then need to experiment to tune the interrupt moderation settings — consult ethtool(8) for details (does not apply if using ibverbs).

  • Disable Ethernet flow control: ethtool -A interface rx off tx off.

  • Use the isolcpus kernel option to completely isolate some CPU cores from other tasks, and pin the receiver to those cores (I have not actually tried this).

  • Use chrt(1) to run the receiver with real-time scheduling (I have not actually tried this).




AMD Epyc tuning

There are also some specific BIOS settings that are important for AMD Epyc systems (these have been tried on Rome and Milan systems):

  • Set APBDIS to 1 and Fixed SOC PState to P0. This prevents the bus from going into low-power states when it thinks there isn’t enough work.

  • Disable DF Cstates.

  • Enable PCIe relaxed ordering.

  • Experiment to find the best NUMA-per-socket setting. NPS4 gives slightly higher throughput but it also seems to let GPUs starve the NIC.

  • When placing cards into slots, be aware that slots that connect to the same quadrant of the CPU (NUMA node, in NPS4 mode) contend for bandwidth to the CPU.

The above have all been observed to make significant differences in spead2 applications. Below are some other general tuning recommendations found on the internet, for which it’s unclear whether it will make a difference:

  • Disable IOMMU.

  • Set local APIC mode to x2APIC.

  • Set Preferred I/O to manual and Preferred I/O bus to the bus containing the NIC (only really useful for a single NIC).

  • On Milan, set LCLK frequency control to the maximum frequency.

Interrupt affinity

NICs typically have multiple send and receive queues with their own interrupt numbers, and each interrupt is typically directed to a particular CPU core. This means that not all CPU cores are equal when it comes to pinning threads. Generally you want the receiver to run “close” to the core handling the interrupts, so that the interrupt handler can wake it up easily, and driver data structures can be cached; but if they are on the same core it can sometimes reduce performance by contending for resources.

The drivers for NVIDIA NICs include some tools (, to show and set IRQ affinities, which can help to set the affinities in a predictable way. Note that for this to be effective, the irqbalance daemon needs to be disabled, as it will try to dynamically adjust IRQ affinities based on usage patterns.

Protocol design

If you are designing a new SPEAD-based protocol, you have an opportunity to make design choices that will make it easier for the sender and/or receiver to reach the desired performance.

Heap size

The primary influence comes from heap size. There is some degree of overhead for every heap (particularly for a Python receiver), and very small heaps will cause this overhead to dominate. Heaps smaller than 16KiB are not recommended. Very large heaps that do not fit into CPU caches will also reduce performance, but not excessively. Memory usage also depends on the heap size. A number of application tuning techniques described below also depend on knowing the heap payload size a priori; thus, it is good practice to communicate this the receiver in some way, whether by sending the descriptor early in the SPEAD stream or by an out-of-band method.

Packet size

Packet size is not strictly part of the protocol, but also has a large impact on performance. For 10Gb/s or faster streams, jumbo frames are highly recommended, although with the kernel bypass techniques described below, this is far less of an issue.

When using spead2 on the send side, the default packet size is 1472 bytes, which is a safe value for IPv4 in a standard Ethernet setup 4. The packet size is set in the StreamConfig. You should pick a packet size, that, when added to the overhead for IP and UDP headers, does not exceed the MTU of the link. For example, with IPv4 and an MTU of 9200, use a packet size of 9172.


The UDP and IP header together add 28 bytes, bringing the IP packet to the conventional MTU of 1500 bytes.

When using TCP/IP, the packet size can be much larger (e.g. 65536) as it no longer corresponds to IP packets.


Because items directly reference the received data (where possible), it is possible that data will be misaligned. While numpy allows this, it could make access to the data inefficient. The sender should ensure that data are aligned. The spead2 sending API currently does not provide a way to enforce this, but using items with round sizes will help.


When using numpy builtin types, data are converted to native endian when they are received, to allow for more efficient operations on them. This can reduce the maximum rate at which packets are received. Thus, using the native endian on the wire (little-endian for x86) will give better performance.

Data format

Item descriptors can be specified using either a format or a dtype (numpy data type). In many common cases, either can be used, and performance on a Python receiver should be the same (a PySPEAD receiver, however, will be much faster with dtype). The dtype is the only way to use Fortran order or little-endian. The format approach is easier for a C++ receiver to parse (since it does not need to decode a Python literal). It also allows for a wider variety of types (such as bit vectors), but encoding or decoding these types in Python takes a very slow path.

Application tuning

This section describes a number of ways the application can be modified to improve performance. Most of these tuning options can be explored using a provided benchmarking tool which measures the sustained performance on a connection. This makes it possible to quickly identify the techniques that will make the most difference before implementing them.

Memory allocation

Using a memory pool is the single most important tool for fast and reliable data transfer. It is particularly important when heap sizes are large enough that malloc() and free() use mmap() (M_MMAP_THRESHOLD in glibc). For very small heaps, memory pooling may be a net loss.

To use a memory pool, it is necessary to know the maximum heap payload size (a conservative estimate is fine too — you will just use more memory). You also need to size the pool appropriately. It is possible to specify a small initial size and a larger maximum; however, each time the pool grows the CPU will be busy with allocation and may drop packets. To avoid starvation, you will need to provide:

  • A buffer per partial heap (max_heaps parameter to spead2.recv.Stream)

  • A buffer per complete heap in the ring buffer (ring_heaps parameter to spead2.recv.Stream)

  • A buffer for every heap that has been taken off the ring buffer but not yet destroyed.

  • A few extra for heaps that are in-flight between queues. The exact number may vary between releases, but 4 should be safe.

In general, it is best to err on the side of adding a few extra, provided that this does not consume too much memory. At present there are unfortunately no good tools for analysing memory pool performance.

Chunking receiver

An alternative to using memory pools is to use the Chunking receiver. It has the same benefit of keeping memory allocated within the application rather than returning it to the OS.

Heap lifetime (Python)

All the payload for a heap is stored in a single memory allocation, and where possible, items reference this memory. This means that the entire heap remains live as long as any of the values encoded in it are live. Thus, a small but seldom-changing value can cause a very large heap to remain live long after the rest of the values in that heap have been replaced. This can waste memory, and also affects memory pool sizing.

To avoid this, senders should try to group items together that are updated at the same frequency, rather than mixing low- and high-frequency items in the same heap. Receivers can avoid this problem by copying values that are known to be slowly varying.

Custom allocators

If you are doing an extra copy purely to put values into a special memory type (for example, shared memory to communicate with another process, or pinned memory for transfer to a GPU), then consider subclassing spead2::memory_allocator (C++ only), or using a Chunking receiver.

Tuning based on heap size

The library has a number of tuning parameters that are reasonable for medium-to-large heaps (megabytes or larger). If using many smaller heaps, some of the tuning parameters may need to be adjusted. In particular

  • Increase the max_heaps parameter to the spead2.send.StreamConfig constructor.

  • Increase the max_heaps parameter to the spead2.recv.Stream constructor if you expect the network to reorder packets significantly (e.g., because data is arriving from multiple senders which are not completely synchronised). For single-packet heaps this has no effect.

  • Increase the ring_heaps parameter to the spead2.recv.Stream constructor to reduce lock contention. This has rapidly diminishing returns beyond about 16.

It is important to experiment to determine good values. Simply cranking everything way up can actually reduce performance by increase memory usage and thus reducing cache efficiency.

For very large heaps (gigabytes) some of these values can be decreased to 2 (or possibly even 1) to keep memory usage under control.

Thread pools

Each stream in spead2 has an associated thread pool, which provides worker threads for handling incoming or outgoing packets. Each thread pool can have some number of threads, defaulting to 1. Here are some rules of thumb:

  • For a small number of streams (up to about the number of CPU cores), it is best to have one single-threaded thread pool per stream. This gives better cache affinity than a shared thread pool.

  • For a large number of lower-bandwidth streams, use a shared thread pool with multiple threads. The number of threads should be chosen based on the number of CPU cores that you can dedicate to packet handling rather than other tasks in your application.

  • A single stream cannot be processed by multiple threads at the same time, so there is never any benefit (and often detriment) to have more threads in a thread pool than there are streams serviced by that thread pool.

  • Jitter (experienced as occasionally lost heaps) can be reduced by passing an affinity list to the thread pool constructor, to pin threads to specific cores. The main thread can be pinned as well, using spead2.ThreadPool.set_affinity().