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The NVIDIA DOCA framework aims to simplify the programming and application development for NVIDIA BlueField DPUs and ConnectX SmartNICs. It provides high-level…

The NVIDIA DOCA framework aims to simplify the programming and application development for NVIDIA BlueField DPUs and ConnectX SmartNICs. It provides high-level abstraction building blocks relevant to network applications through an SDK, runtime binaries, and high-level APIs that enable developers to rapidly create applications and services.

NVIDIA DOCA Flow is a newly updated set of software drivers and a steering library in the DOCA Framework. It runs in user space and enables offloading of networking-related operations from the CPU. This in turn enables applications to process high-packet throughput workloads with low latency, conserving CPU resources and reducing power usage. 

DOCA Flow also efficiently optimizes the utilization of BlueField DPUs and ConnectX SmartNICs. DOCA is the key to unlocking the potential of BlueField’s acceleration engines, while DOCA Flow grants rapid access to the acceleration engine for packet-steering logic.

Simplify and expedite development

DOCA Flow offers C library APIs for defining hardware-based packet processing pipelines​, abstracting the hardware capabilities of BlueField DPUs and ConnectX SmartNICs. This enables developers to construct high-performance and scalable applications for data center and cloud networks, programmatically defining and controlling network traffic flows, implementing network policies, and efficiently managing resources.

DOCA Flow complements and expands upon the core programming capabilities of DPDK, providing additional optimized features tailored specifically for NVIDIA DPUs and NICs. Further, DOCA Flow simplifies the complexities of the networking stack by offering building blocks for implementing basic packet processing pipelines for popular networking use cases, as well as for more sophisticated ones such as Longest Prefix Matching (LPM), Internet Protocol Security (IPsec) encryption or decryption, and creation or modification of entries in the Access Control List (ACL).

Using pre-created networking building blocks enables you to focus on creating your applications instead of writing low-level packet processing routines. This reduces TTM and frees you to focus on the core of your application, as the building blocks are already efficiently optimized for performance. DOCA Flow building blocks make software development easier, and can be used by developers of all experience levels.

Why DPUs are needed

Modern workloads and software-defined networking lead to significant networking overhead on CPU cores. Data centers and cloud networks now start at speeds of 25 or 100 Gbps and scale up to 200 and even 400 Gbps, requiring CPU cores to handle classification, tracking, processing, and steering of network traffic at immense speeds. 

Compute virtualization amplifies networking requirements by generating more east/west traffic internally between host VMs and containers, and adding overlay network encapsulation plus microsegmentation to external communication with other servers or storage devices. As a result, much more networking demand is placed on the CPU.

CPU cores come with a significant cost and are not well-suited for efficient network packet processing. High-bandwidth tasks consume more CPU cores, placing unnecessary strain on the server’s valuable compute infrastructure, which could otherwise be utilized more productively for tenant workloads and application data processing. 

In contrast, specialized hardware like SmartNICs and DPUs are specifically designed to efficiently handle fast data movement at scale, with reduced power consumption, heat, and overall cost compared to standard CPUs.

Execution pipes

The DOCA Flow library provides APIs that use hardware functions within the BlueField DPU and ConnectX SmartNICs to build generic and reusable execution pipes, where each pipe may consist of match criteria (packet classification) and a set of actions. 

​Classification enables identifying incoming packets to which logic should be applied, while actions vary and implement the logic that fits each packet’s classification. Using classifications and actions as building blocks provides a flexible method for developing hardware-accelerated network applications including gateways, firewalls, load-balancers, and others.

As mentioned, the actions in the DOCA Flow execution pipe vary and may include, as an example, packet manipulations such as applying Network Address Translation (NAT) logic on MAC addresses, changing source or destination IP addresses, applying overlay network encapsulation, changing header fields, incrementing a counter to measure traffic, and more. Actions may include monitoring traffic through the use of policies, forwarding traffic to different queues–either software queues or hairpin targets, port mirroring or packet sampling for debugging and lawful interception, and dropping packets to enforce policies or access control–all completely offloaded to the DPU or NIC hardware.

Pipes may be chained together through forwarding actions from one pipe to another to form a complete steering tree that defines paths for incoming packets. After executing a predefined action on a packet, the packet may be forwarded to another pipe for further action or inspection, to a software queue, to a hardware hairpin queue, or sent down the wire or dropped.

Steering trees

A steering tree can be used to create hardware-based network applications on a DPU or NIC by implementing common network function logic. This enables packets to be effectively categorized so the appropriate action can be applied to each one. Using the steering tree concept provides multiple benefits, including: 

• Customized processing logic for each data flow
• Versatility in directing packets to specific actions or destinations
• Adaptable structure that’s easily resized for changing conditions
• Flexible framework that allows adding new pipe types to meet evolving requirements 
• Optimized resource use, minimizing redundancy and enabling shared matches and actions 

NVIDIA DOCA Flow use cases

When developing network pipelines for BlueField DPUs and ConnectX SmartNICs, DOCA Flow serves as a fundamental element in simplifying application development efforts. Use cases are applicable across enterprise data centers, telco, and cloud environments, particularly those that are focused on network infrastructure and security that require efficient packet processing. 

Additionally, it is designed to handle scenarios involving establishing and removing pipelines at an extremely high rate, and can manage millions of packet exchanges per second. This accommodates software-defined networking applications, data analytics, virtual switching, AI inferencing, cybersecurity, and other packet processing applications. It enables operations like ingesting, examining headers and payloads, tracking connections, and inspecting, rerouting, copying, or dropping packets based on predetermined policies or other criteria.

Open vSwitch virtual switching

Open vSwitch (OVS) enables massive network automation through programmatic extension, and is designed to enable efficient network switching in virtualized environments such as virtual machines (VMs) and containers. Through DOCA Flow, a DPU-accelerated virtual switch (vSwitch) can be implemented in the user space data plane, allowing any server with a DPU to act as a network switch, router, or stateful load balancer. 

This provides the flexibility of having a vSwitch available to multiple VNFs, while also significantly increasing small packet throughput and reducing latencies, thus expediting and accelerating communication through enhanced networking performance from the DPU and facilitating both north-south traffic for connecting to users, and east-west traffic for AI and distributed applications.

Next-generation firewall 

Modern firewalls are required to inspect data at higher rates in order to combat new threats. However, with increased network speeds, more load is placed on the CPU. This can lead to increased latency, dropped packets, and reduced network throughput. To support higher speeds and more stringent security requirements without sacrificing latency is complex and deploying enough traditional firewalls capable of handling the increased traffic is cost-prohibitive.

DOCA Flow enables the development of an intelligent network filter for each server hosting a DPU. With this filter, the parsing and steering of traffic flows is based on predefined policies, with no CPU overhead. It can be used to create a distributed next-generation firewall (NGFW) that can achieve close to 100 Gbps throughput per server by using dedicated accelerators and Arm cores on the DPU to filter and forward packets based on their appropriate flow, as well as managing the data plane offloads and control plane of the NGFW.

Using DOCA Flow can provide a cost-effective solution to offload packet processing from the CPU to the DPU to increase performance and reduce costs beyond traditional hardware solutions. It offers advanced security features, like intrusion prevention, without sacrificing server performance. It also enables faster network flow inspection within the NIC/DPU.

Virtual network functions

DOCA Flow can accelerate virtualized network functions (VNFs), such as routers, load balancers, firewalls, content delivery network (CDN) services, and more. Telco vendors can replace proprietary hardware and execute virtualized workloads on commodity servers by developing VNFs that run on BlueField DPUs. 

By using DPUs for VNF acceleration, a more efficient and flexible solution is achieved, leading to reduced equipment, space, heat, and power requirements compared with commodity servers. All this helps resolve limitations based on cooling and space constraints, enabling new opportunities with 5G, AI, IoT, and edge computing.

Edge applications

DOCA Flow is an ideal solution for edge workloads that require high network speeds and I/O processing capabilities, such as content delivery networks and video analytics systems. Host applications for the edge can be designed using DOCA Flow to run on DPUs installed within general-purpose servers, eliminating the need for expensive proprietary hardware appliances. By utilizing the DPUs acceleration and Arm cores, fewer server CPU cores are needed, allowing for smaller servers that consume less energy, require less cooling, and occupy less rack space. This approach offers cost savings in terms of both CapEx and OpEx.

Summary

The DOCA Flow library can be instrumental for developers by simplifying the development of modern applications that offer accelerated network throughput and latency improvements in packet processing. This is especially true for applications replacing proprietary bare-metal hardware solutions with virtualized applications hosted on commercial-off-the-shelf (COTS) server platforms. 

The library consists of several building blocks for efficient networking offloads, including implementing basic packet processing pipelines, Longest Prefix Matching (LPM), and Internet Protocol Security (IPsec) encryption or decryption. Enhancements will be added soon to Connection Tracking (CT) and Access Control List (ACL) to create or modify access control entries. For a sampling of DOCA Flow reference applications, see the DOCA Reference Applications documentation. 

By harnessing the capabilities of DOCA Flow, organizations can minimize cost, accelerate service deployment, and optimize hardware utilization within use cases that demand high throughput and low latency.

To learn more, explore the following resources:

• Transform the Data Center for the AI Era with NVIDIA DPUs and NVIDIA DOCA
• BlueField DPUs with DOCA
• Transforming IPsec Deployments with NVIDIA DOCA 2.0
• Accelerated Networking
• Transformation with BlueField DPUs

NVIDIA today reported revenue for the second quarter ended July 30, 2023, of $13.51 billion, up 101% from a year ago and up 88% from the previous quarter.

Delve into how TMA Solutions is accelerating original ML and AI workflows with RAPIDS.

Delve into how TMA Solutions is accelerating original ML and AI workflows with RAPIDS.

The advent of cloud computing has ushered in a paradigm shift in our data storage and utilization practices. Businesses can bypass the complexities of managing…

The advent of cloud computing has ushered in a paradigm shift in our data storage and utilization practices. Businesses can bypass the complexities of managing their own computing infrastructure by tapping into remote, on-demand resources deftly managed by cloud service providers. Yet, there exists a palpable apprehension about sharing sensitive information with the cloud.

While traditional cryptographic methods such as AES can help preserve data privacy, they stifle the cloud’s capacity to undertake meaningful operations on the data. In an encryption system, a message, also referred to as plaintext, is transformed using a key into a scrambled version known as ciphertext. The encryption methods in place assure that malicious entities can’t decrypt the message without the correct key and can’t gain access to its contents. Without decryption, a ciphertext is essentially nonsensical.

Fortunately, there is a solution: fully homomorphic encryption (FHE), a sophisticated and potent encryption technique. It serves as a comprehensive shield for data, whether in transit, at rest, or in use. Homomorphic encryption is a unique design that enables operations to be performed on the ciphertext while still preserving its security. This type of operation modifies a ciphertext in such a way that, after decryption, the result is the same as if the operation was carried out on the plaintext itself.

Frequently referred to as the ultimate goal of cryptography, FHE can carry out any computational function on the ciphertext. This remarkable technology has the capability to run thousands of algorithms directly on encrypted data, all without compromising the underlying plaintext. It confronts the pivotal privacy issues linked with cloud computing head-on, while simultaneously enabling intricate outsourced computations.

However, operations performed with FHE encryption tend to be slower than those conducted over standard unencrypted data. Also, the current applications of this technology can be somewhat challenging for non-specialists to set up and manage effectively.

ArctyrEX: Accelerated encrypted execution

A recent effort by NVIDIA Research aims to address the efficiency problems associated with encrypted computing. The proposed solution, ArctyrEX, is an end-to-end framework that enables you to specify algorithms in C++ using standard data types and automatically convert this algorithm into an FHE representation that can be launched on arbitrary numbers of GPUs for efficient evaluation.

ArctyrEX is composed of three parts:

• A frontend responsible for converting input programs to the encrypted domain
• A runtime library that sends encrypted workloads to GPU workers
• A backend that consists of an implementation of the state-of-the-art CGGI cryptosystem.

The CGGI cryptosystem encrypts single bits of plaintext and enables encrypted Boolean operations between ciphertexts. Therefore, the programming model for CGGI is akin to constructing Boolean circuits composed of logic gates. For more information, see TFHE: Fast Fully Homomorphic Encryption over the Torus and TFHE.

The frontend takes advantage of decades of hardware development research to convert C++ programs to equivalent Verilog files, which provide a description of a circuit at a high level of abstraction. Then it converts the files to optimal Boolean circuits. This process uses well-established techniques in the form of high-level synthesis (HLS) and register-transfer level (RTL) or logic synthesis.

The ArctyrEX runtime library takes the generated Boolean circuit, divides it into distinct levels, and creates batches of approximately identical size to distribute to GPU workers. In some cases, the ciphertexts corresponding to an output wire of a gate at an intermediate level may have to serve as input to a workload assigned to a different GPU during the next level. ArctyrEX is capable of seamlessly coordinating these transfers efficiently.

Lastly, the ArctyrEX backend consists of an optimized CUDA kernel capable of executing batches of gates concurrently while minimizing CPU-GPU synchronizations. The output is composed of a set of ciphertexts that represent the output wires of the gates in the assigned batch.

Run programs with ArctyrEX up to 40x faster

ArctyrEX shows great scalability with the use of more GPUs for two key reasons:

• Abundant primitive-level parallelism that is a natural trait of most FHE operations.
• Circuit-level parallelism, which is a characteristic commonly displayed in Boolean circuits.

To attain speedy evaluation with Boolean FHE, the circuit must have wide levels, meaning that there should be many gates that can operate simultaneously, and it should have a minimal critical path. To show this, compare an encrypted dot product operation between two vectors with an encrypted vector addition operation.

To actualize these programs in ArctyrEX, you can write C++ code at a high level in an intuitive manner. Here, the pragma is necessary to unroll the loop before the HLS pass. You can employ HLS tools like Google XLS, which are designed to make digital design more approachable and efficient. XLS provides the means to specify digital logic at a higher level like C++ than traditional hardware description languages (like Verilog or VHDL), making the entire process simpler and more efficient.

As an example, observe the differences between an encrypted dot product between two vectors and an encrypted vector addition. To implement these programs in ArctyrEX, you can intuitively write high-level C++ code in the following way, where the pragma is required to unroll the loop before the HLS pass handled by Google XLS:

void vector_addition(int x[500], int y[500], int z[500]) {
  #pragma hls_unroll yes
  for (int i =0; i 

Figure 3 shows the characteristics of both circuits, specifically the number of logic gates at each level of the circuit. In the chart, |v| indicates the vector length and M refers to the dimensions of the matrices. You can see that a dot product of two vectors has a higher critical path with a depth of approximately 120 levels and the width of subsequent levels decreases gradually, limiting the parallelism possible to exploit in the later stages of the circuit.

On the other hand, vector addition has a critical path that is approximately 2x shorter than the dot product, and the width of each level remains relatively constant. As a result, the vector addition runs approximately 4x faster than the dot product, although both exhibit high speedups over a parallel CPU baseline with multiple GPUs. These interesting performance trends are shown in the topology graphs earlier. The light green bars show the speedup relative to a 256-threaded CPU baseline while the dark green bars show the execution time of the homomorphic application.

Summary

In summary, ArctyrEX represents a comprehensive solution for encrypted computation across various applications, harnessing the power of GPU acceleration and implementing innovative techniques for efficient FHE algorithm execution. Tasks like neural network inference demonstrate linear acceleration with the augmentation of GPUs, attributed to the inherent circuit-level parallelism, the newly introduced dispatch paradigm, and the extensive primitive-level parallelism harnessed by the CUDA-accelerated CGGI backend.

For more information, see the Accelerated Encrypted Execution of General-Purpose Applications paper and the live NVIDIA GTC 2023 presentation, ArctyrEX: Accelerated Encrypted Execution of General-Purpose Applications on GPUs.

Acknowledgments

We thank all authors of ArctyrEX for their contributions, especially Charles Gouert (Ph.D. candidate) and Prof. Nektarios Georgios Tsoutsos from the University of Delaware. We’d also like to thank members of Zama, CryptoLab, Google, and Duality Tech for their insightful discussions, particularly Ilaria Chillotti, Jung Hee Cheon, Ahmad Al Badawi, Yuri Polyakov, David Cousins, Shruti Gorantala, and Eric Astor.

Heterogeneous Memory Management (HMM) is a CUDA memory management feature that extends the simplicity and productivity of the CUDA Unified Memory programming…

Heterogeneous Memory Management (HMM) is a CUDA memory management feature that extends the simplicity and productivity of the CUDA Unified Memory programming model to include system allocated memory on systems with PCIe-connected NVIDIA GPUs. System allocated memory refers to memory that is ultimately allocated by the operating system; for example, through malloc, mmap, the C++ new operator (which of course uses the preceding mechanisms), or related system routines that set up CPU-accessible memory for the application. 

Previously, on PCIe-based machines, system allocated memory was not directly accessible by the GPU. The GPU could only access memory that came from special allocators such as cudaMalloc or cudaMallocManaged. 

With HMM enabled, all application threads (GPU or CPU) can directly access all of the application’s system allocated memory. As with Unified Memory (which can be thought of as a subset of, or precursor to HMM), there is no need to manually copy system allocated memory between processors. This is because it is automatically placed on the CPU or GPU, based on processor usage.

Within the CUDA driver stack, CPU and GPU page faults are typically used to discover where the memory should be placed. Again, this automatic placement already happens with Unified Memory—HMM simply extends the behavior to cover system allocated memory as well as cudaMallocManaged memory.

This new ability to directly read or write to the full application memory address space will significantly improve programmer productivity for all programming models built on top of CUDA: CUDA C++, Fortran, standard parallelism in Python, ISO C++, ISO Fortran, OpenACC, OpenMP, and many others. 

In fact, as the upcoming examples demonstrate, HMM simplifies GPU programming to the point that GPU programming is nearly as accessible as CPU programming. Some highlights:

• Explicit memory management is not required for functionality when writing a GPU program; therefore, an initial “first draft” program can be small and simple. Explicit memory management (for performance tuning) can be deferred to a later phase of development.
• GPU programming is now practical for programming languages that do not distinguish between CPU and GPU memory.
• Large applications can be GPU-accelerated without requiring large memory management refactoring, or changes to third-party libraries (for which source code is not always available).

As an aside, new hardware platforms such as NVIDIA Grace Hopper natively support the Unified Memory programming model through hardware-based memory coherence among all CPUs and GPUs. For such systems, HMM is not required, and in fact, HMM is automatically disabled there. One way to think about this is to observe that HMM is effectively a software-based way of providing the same programming model as an NVIDIA Grace Hopper Superchip.

To learn more about CUDA Unified Memory, see the resources section at the end of this post.

Unified Memory before HMM

The original CUDA Unified Memory feature introduced in 2013 enables you to accelerate a CPU program with only a few changes, as shown below:

void sortfile(FILE* fp, int N) {
  char* data;
  data = (char*)malloc(N);

  fread(data, 1, N, fp);
  qsort(data, N, 1, cmp);


  use_data(data);
  free(data);
}

void sortfile(FILE* fp, int N) {
  char* data;
  cudaMallocManaged(&data, N);

  fread(data, 1, N, fp);
  qsort>>(data, N, 1, cmp);
  cudaDeviceSynchronize();

  use_data(data);
  cudaFree(data);
}

This programming model is simple, clear, and powerful. Over the past 10 years, this approach has enabled countless applications to easily benefit from GPU acceleration. And yet, there is still room for improvement: note the need for a special allocator: cudaMallocManaged, and the corresponding cudaFree.

What if we could go even further, and get rid of those? That’s exactly what HMM does.

Unified Memory after HMM

On systems with HMM (detailed below), continue using malloc and free:

void sortfile(FILE* fp, int N) {
  char* data;
  data = (char*)malloc(N);

  fread(data, 1, N, fp);
  qsort(data, N, 1, cmp);


  use_data(data);
  free(data);
}

void sortfile(FILE* fp, int N) {
  char* data;
  data = (char*)malloc(N);

  fread(data, 1, N, fp);
  qsort>>(data, N, 1, cmp);
  cudaDeviceSynchronize();

  use_data(data);
  free(data)
}

With HMM, the memory management is now identical between the two.

System allocated memory and CUDA allocators

GPU applications using CUDA memory allocators work “as is” on systems with HMM. The main difference in these systems is that system allocation APIs like malloc, C++ new, or mmap now create allocations that may be accessed from GPU threads, without having to call any CUDA APIs to tell CUDA about the existence of these allocations. Table 1 captures the differences between the most common CUDA memory allocators on systems with HMM: 

In general, selecting the allocator that better expresses the application intent may enable CUDA to deliver better performance. With HMM, these choices become performance optimizations that do not need to be done upfront, before accessing the memory from the GPU for the first time. HMM enables developers to focus on parallelizing algorithms first, and performing memory allocator-related optimizations later, when their overhead improves performance. 

Seamless GPU acceleration for C++, Fortran, and Python

HMM makes it significantly easier to program NVIDIA GPUs with standardized and portable programming languages like Python that do not distinguish between CPU and GPU memory and assume all threads may access all memory, as well as programming languages described by international standards like ISO Fortran and ISO C++. 

These languages provide concurrency and parallelism facilities that enable implementations to automatically dispatch computations to GPUs and other devices. For example, since C++ 2017, the standard library algorithms from the header accept execution policies that enable implementations to run them in parallel.

Sorting a file in place from the GPU

For example, before HMM, sorting a file larger than CPU memory in-place was complicated, requiring sorting smaller parts of the file first, and merging them into a fully-sorted file afterwards. With HMM, the application may map the file on disk into memory using mmap, and read and write to it directly from the GPU. For more details, see the HMM sample code file_before.cpp and file_after.cpp on GitHub.

void sortfile(FILE* fp, int N) {
  std::vector buffer;
  buffer.resize(N);
  fread(buffer.data(), 1, N, fp);
  
  // std::sort runs on the GPU:
  std::sort(std::execution::par,
    buffer.begin(), buffer.end(),
    std::greater{});
  use_data(std::span{buffer});
}

void sortfile(int fd, int N) {
  auto buffer = (char*)mmap(NULL, N, 
     PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);

    
  // std::sort runs on the GPU: 
  std::sort(std::execution::par,
    buffer, buffer + N,
    std::greater{});
  use_data(std::span{buffer});
}

The NVIDIA C++ Compiler (NVC++) implementation of the parallel std::sort algorithm sorts the file on the GPU when using the -stdpar=gpu option. There are many restrictions on the use of this option, as detailed in the HPC SDK documentation. 

• Before HMM: GPU may only access dynamically allocated memory on the heap within code compiled by NVC++. That is, automatic variables on CPU thread stacks, global variables, and memory-mapped files are not accessible from the GPU (see examples below).
• After HMM: GPU may access all system allocated memory, including data dynamically allocated on the heap in CPU code compiled by other compilers and third-party libraries, automatic variables on CPU thread stacks, global variables in CPU memory, memory-mapped files, and so on

Atomic memory operations and synchronization primitives

HMM supports all memory operations, which includes atomic memory operations. That is, programmers may use atomic memory operations to synchronize GPU and CPU threads with flags. While certain parts of the C++ std::atomic APIs use system calls that are not available on the GPU yet, such as std::atomic::wait and std::atomic::notify_all/_one APIs, most of the C++ concurrency primitive APIs are available and readily useful to perform message passing between GPU and CPU threads.

For more information, see the documentation of HPC SDK C++ Parallel Algorithms: Interoperability with the C++ Standard Library) and  atomic_flag.cpp HMM sample code on GitHub. You can extend this set using CUDA C++. See the ticket_lock.cpp HMM sample code on GitHub for more details.

void main() {
  // Variables allocated with cudaMallocManaged
  std::atomic* flag;
  int* msg;
  cudaMallocManaged(&flag, sizeof(std::atomic));
  cudaMallocManaged(&msg, sizeof(int));
  new (flag) std::atomic(0);
  *msg = 0;
 
  // Start a different CPU thread…
  auto t = std::jthread([&] { 
    // … that launches and waits 
    // on a GPU kernel completing
    std::for_each_n(
      std::execution::par, 
      &msg, 1, [&](int& msg) {
        // GPU thread writes message…
        *msg = 42;       // all accesses via ptrs
        // …and signals completion…
        flag->store(1);  // all accesses via ptrs
    });
  });
 
  // CPU thread waits on GPU thread
  while (flag->load() == 0); // all accesses via ptrs
  // …and reads the message:
  std::cout 

void main() {
  // Variables on CPU thread stack:
  std::atomic flag = 0;  // Atomic
  int msg = 0;                // Message
 
  


// Start a different CPU thread…
  auto t = std::jthread([&] { 
    // … that launches and waits 
    // on a GPU kernel completing
    std::for_each_n(
      std::execution::par, 
      &msg, 1, [&](int& msg) {
        // GPU thread writes message…
        msg = 42;
        // …and signals completion…
        flag.store(1);  
    });
  });
 
  // CPU thread waits on GPU thread
  while (flag.load() == 0);
  // …and reads the message:
  std::cout 

void main() {
  // Variables allocated with cudaMallocManaged
  ticket_lock* lock;    // Lock
  int* msg;         // Message
  cudaMallocManaged(&lock, sizeof(ticket_lock));
  cudaMallocManaged(&msg, sizeof(int));
  new (lock) ticket_lock();
  *msg = 0;

  // Start a different CPU thread…
  auto t = std::jthread([&] {
    // … that launches and waits 
    // on a GPU kernel completing
    std::for_each_n(
      std::execution::par, 
      &msg, 1, [&](int& msg) {
        // GPU thread takes lock…
        auto g = lock->guard();
        // … and sets message (no atomics)
        msg += 1;
    }); // GPU thread releases lock here
  });
  
  { // Concurrently with GPU thread
    // … CPU thread takes lock…
    auto g = lock->guard();
    // … and sets message (no atomics)
    msg += 1;
  } // CPU thread releases lock here

  t.join();  // Wait on GPU kernel completion
  std::cout 

void main() {
  // Variables on CPU thread stack:
  ticket_lock lock;    // Lock
  int msg = 0;         // Message

  



  // Start a different CPU thread…
  auto t = std::jthread([&] {
    // … that launches and waits 
    // on a GPU kernel completing
    std::for_each_n(
      std::execution::par, 
      &msg, 1, [&](int& msg) {
        // GPU thread takes lock…
        auto g = lock.guard();
        // … and sets message (no atomics)
        msg += 1;
    }); // GPU thread releases lock here
  });
  
  { // Concurrently with GPU thread
    // … CPU thread takes lock…
    auto g = lock.guard();
    // … and sets message (no atomics)
    msg += 1;
  } // CPU thread releases lock here

  t.join();  // Wait on GPU kernel completion
  std::cout 

Accelerate complex HPC workloads with HMM

Research groups working on large and long-lived HPC applications have yearned for years for more productive and portable programming models for heterogeneous platforms. m-AIA is a multi-physics solver spanning almost 300,000 lines of code developed at the Institute of Aerodynamics at RWTH Aachen, Germany. See Accelerating a C++ CFD Code with OpenACC for more information. Instead of using OpenACC for the initial prototype, it is now partially accelerated on GPUs using the ISO C++ programming model described above, which was not available when the prototype work was done.

HMM enabled our team to accelerate new m-AIA workloads that interface with GPU-agnostic third-party libraries such as FFTW and pnetcdf, which are used for initial conditions and I/O and are oblivious to the GPU directly accessing the same memory.

Leverage memory-mapped I/O for fast development 

One of the interesting features that HMM provides is memory-mapped file I/O directly from the GPU. It enables developers to directly read files from supported storage or /disk without staging them in system memory and without copying the data to the high bandwidth GPU memory. This also enables application developers to easily process input data larger than the available physical system memory, without constructing an iterative data ingestion and computation workflow.

To demonstrate this functionality, our team wrote a sample application that builds a histogram of hourly total precipitation for every day of the year from the ERA5 reanalysis dataset. For more details, see The ERA5 global reanalysis.

The ERA5 dataset consists of hourly estimates of several atmospheric variables. In the dataset, total precipitation data for each month is stored in a separate file. We used 40 years of total precipitation data from 1981–2020, which sum to 480 input files aggregating to ~1.3 TB total input data size. See Figure 1 for example results.

Using the Unix mmap API, input files can be mapped to a contiguous virtual address space. With HMM, this virtual address can be passed as input to a CUDA kernel which can then directly access the values to build a histogram of total precipitation for each hour for all the days in a year. 

The resulting histogram will reside in GPU memory and can be used to easily compute interesting statistics such as average monthly precipitation over the northern hemisphere. As an example, we also computed average hourly precipitation for the months of February and August. To see the code for this application, visit HMM_sample_code on GitHub.

size_t chunk_sz = 70_gb;
std::vector buffer(chunk_sz);

for (fp : files)
  for (size_t off = 0; off 
  
    histogram>>(dev, N, out);
    cudaDeviceSynchronize();
  }

void* buffer = mmap(NULL, alloc_size,
                    PROT_NONE, MAP_PRIVATE | MAP_ANONYMOUS, 
                    -1, 0);
for (fd : files)
  mmap(buffer+file_offset, fileByteSize, 
       PROT_READ, MAP_PRIVATE|MAP_FIXED, fd, 0);


histogram>>(buffer, total_N, out);
cudaDeviceSynchronize();

Enabling and detecting HMM

The CUDA Toolkit and driver will automatically enable HMM whenever it detects that your system can handle it. The requirements are documented in detail in the CUDA 12.2 Release Notes: General CUDA. You’ll need:

• NVIDIA CUDA 12.2 with the open-source r535_00 driver or newer. See  NVIDIA Open GPU Kernel Modules Installation Documentation for details.
• A sufficiently recent Linux kernel: 6.1.24+, 6.2.11+, or 6.3+.
• A GPU with one of the following supported architectures: NVIDIA Turing, NVIDIA Ampere, NVIDIA Ada Lovelace, NVIDIA Hopper, or newer.
• A 64-bit x86 CPU.

Query the Addressing Mode property to verify that HMM is enabled:

$ nvidia-smi -q | grep Addressing
Addressing Mode : HMM

To detect systems in which GPUs may access system allocated memory, query the cudaDevAttrPageableMemoryAccess. 

In addition, systems such as the NVIDIA Grace Hopper Superchip support ATS, which has similar behavior to HMM. In fact, the programming model for HMM and ATS systems is the same, so merely checking for cudaDevAttrPageableMemoryAccess suffices for most programs. 

However, for performance tuning and other advanced programming, it is possible to discern between HMM and ATS by also querying for cudaDevAttrPageableMemoryAccessUsesHostPageTables. Table 2 shows how to interpret the results.

For portable applications that are only interested in querying whether the programming model exposed by HMM or ATS is available, querying the ‘pageable memory access’ property usually suffices. 

Unified Memory performance hints

There are no changes to the semantics of pre-existing Unified Memory performance hints. For applications that are already using CUDA Unified Memory on hardware-coherent systems like NVIDIA Grace Hopper, the main change is that HMM enables them to run “as is” on more systems within the limitations mentioned above.

The pre-existing Unified Memory hints also work with system allocated memory on HMM systems:

1. __host__ cudaError_t
   cudaMemPrefetchAsync(* ptr, size_t nbytes, int device):
   asynchronously prefetches memory to a GPU (GPU device ID) or CPU (cudaCpuDeviceId).
2. __host__ cudaError_tcudaMemAdvise(*ptr, size_t nbytes, cudaMemoryAdvise, advice, int device): hints the system about:

• A preferred location for the memory: cudaMemAdviseSetPreferredLocation, or
• A device that will access the memory: cudaMemAdviseSetAccessedBy, or
• A device that will be mostly reading the memory that will be infrequently modified:
  cudaMemAdviseSetReadMostly.

A little more advanced: there is a new CUDA 12.2 API, cudaMemAdvise_v2, that enables applications to choose which NUMA node a given memory range should prefer. This comes into play when HMM places the memory contents on the CPU side.

As always, memory management hints may either improve or degrade performance. Behavior is application and workload dependent, but none of the hints impacts the correctness of the application.

Limitations of HMM in CUDA 12.2

The initial HMM implementation in CUDA 12.2 delivers new features without regressing the performance of any pre-existing applications. The limitations of HMM in CUDA 12.2 are documented in detail in the CUDA 12.2 Release Notes: General CUDA. The main limitations are:

• HMM is only available for x86_64, and other CPU architectures are not yet supported. 
• HMM on HugeTLB allocations is not supported.
• GPU atomic operations on file-backed memory and HugeTLBfs memory are not supported.
• fork(2) without a following exec(3) is not fully supported.
• Page migrations are handled in chunks of 4 KB-page size.

Stay tuned for future CUDA driver updates that will address HMM limitations and improve performance.

Summary

HMM simplifies the programming model by removing the need for explicit memory management for GPU programs that run on common PCIe-based (x86, typically) computers. Programmers can simply use malloc, C++ new, and mmap calls directly, just as they already do for CPU programming.

HMM further boosts programmer productivity by enabling a wide variety of standard programming language features to be safely used within CUDA programs. There is no need to worry about accidentally exposing system allocated memory to a CUDA kernel. 

HMM enables a seamless transition to and from the new NVIDIA Grace Hopper Superchip, and similar machines. On PCIe-based machines, HMM provides the same simplified programming model as that used on the NVIDIA Grace Hopper Superchip.

Unified Memory resources

To learn more about CUDA Unified Memory, the following blog posts will help bring you up to date. You can also join the conversation at the NVIDIA Developer Forum for CUDA.

• An Easy Introduction to CUDA C and C++ (2012)
• Unified Memory in CUDA 6 (2013)
• An Even Easier Introduction to CUDA (2017)
• Unified Memory for CUDA Beginners (2017)

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