CMS-MLG-23-001

CMS-MLG-23-001 ; CERN-EP-2023-303
Portable acceleration of CMS computing workflows with coprocessors as a service
CMS Collaboration
23 February 2024
Submitted to Computing and Software for Big Science
Abstract: Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.
Links: e-print arXiv:2402.15366 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ;

Figures & Tables	Summary	References	CMS Publications

Figures
png pdf	Figure 1: An example inference as a service setup with multiple coprocessor servers. Clients usually run on CPUs, shown on the left side; servers hosting different models run on coprocessors, shown on the right side.
png pdf	Figure 2: Illustration of the SONIC implementation of IaaS in CMSSW. The figure also shows the possibility of an additional load-balancing layer in the SONIC scheme. For example, if multiple coprocessor-enabled machines are used to host servers, a Kubernetes engine can be set up to distribute inference calls across the machines [84]. Image adapted from Ref. [63].
png pdf	Figure 3: Illustration of the jet information in the Run 2 simulated $ \mathrm{t} \overline{\mathrm{t}} $ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin.
png pdf	Figure 3-a: Illustration of the jet information in the Run 2 simulated $ \mathrm{t} \overline{\mathrm{t}} $ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin.
png pdf	Figure 3-b: Illustration of the jet information in the Run 2 simulated $ \mathrm{t} \overline{\mathrm{t}} $ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin.
png pdf	Figure 3-c: Illustration of the jet information in the Run 2 simulated $ \mathrm{t} \overline{\mathrm{t}} $ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin.
png pdf	Figure 3-d: Illustration of the jet information in the Run 2 simulated $ \mathrm{t} \overline{\mathrm{t}} $ data set used in subsequent studies. Distributions of the number of jets per event (left) and the number of particles per jet (right) are shown for AK4 jets (upper) and AK8 jets (lower). For the distributions of the number of particles, the rightmost bin is an overflow bin.
png pdf	Figure 4: Average processing time (left) and throughput (right) of the PN-AK4 algorithm served by a TRITON server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: ONNX (orange), ONNX with TRT (green), and PYTORCH (red). Performance values for these backends when running on a CPU-based TRITON server are given in dashed lines, with the same color-to-backend correspondence.
png pdf	Figure 5: Average processing time (left) and throughput (right) of one of the AK8 ParticleNet algorithms served by a TRITON server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: ONNX (orange), ONNX with TRT (green), and PYTORCH (red). Performance values for these backends when running on a CPU-based TRITON server are given in dashed lines, with the same color-to-backend correspondence.
png pdf	Figure 6: Average processing time (left) and throughput (right) of the DeepMET algorithm served by a TRITON server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Similar performance when running on a CPU-based TRITON server is given in dashed lines.
png pdf	Figure 7: Average processing time (left) and throughput (right) of the DeepTau algorithm served by a TRITON server running on one NVIDIA Tesla T4 GPU, presented as a function of the batch size. Values are shown for different inference backends: TENSORFLOW (TF) (orange), and TENSORFLOW with TRT (blue). Performance values for these backends when running on a CPU-based TRITON server are given in dashed lines, with the same color-to-backend correspondence.
png pdf	Figure 8: The GPU saturation scan performed in GCP, where the per-event throughput is shown as a function of the number of parallel CPU clients for the PYTORCH version of PN-AK4 (black), DeepMET (blue), DeepTau optimized with TRT (red), and all PYTORCH versions of PN-AK8 on a single GPU (green). Each of the parallel jobs was run in a four-threaded configuration. The CPU tasks ran in four-threaded GCP VMs, and the TRITON servers were hosted on separate single GPU VMs also in GCP. The line for direct-inference jobs represents the baseline configuration measured by running all algorithms without the use of the SONIC approach or any GPUs. Each solid line represents running one of the specified models on GPU via the SONIC approach.
png pdf	Figure 9: Production tests across different sites. The CPU tasks always run at Purdue, while the servers with GPU inference tasks for all the models run at Purdue (blue) and at GCP in Iowa (red). The throughput values are higher than those shown in Fig. 8 because the CPUs at Purdue are more powerful than those comprising the GCP VMs.
png pdf	Figure 10: Scale-out test results on Google Cloud. The average throughput of the workflow with the SONIC approach is 4.0 events/s (solid blue), while the average throughput of the direct-inference workflow is 3.5 events/s (dashed red).
png pdf	Figure 11: Throughput (upper) and throughput ratio (lower) between the SONIC approach and direct inference in the local CPU tests at the Purdue Tier-2 cluster. To ensure the CPUs are always saturated, the number of threads per job multiplied by the number of jobs is set to 20.

Tables
png pdf	Table 1: Average event size of different CMS data tiers with Run 2 data-taking conditions [81,69,78].
png pdf	Table 2: The average time of the Mini-AOD processing (without the SONIC approach) with one thread on one single CPU core. The average processing times of the algorithms supported by the SONIC approach are listed in the column labeled ``Time.'' The column labeled ``Fraction'' refers to the fraction of the full workflow's processing time that the algorithm in question consumes. Together, the algorithms currently supported by the SONIC approach consume about 9% of the total processing time. This table also contains the expected server input for each model type created per event in Run 2 $ \mathrm{t} \overline{\mathrm{t}} $ events in the column labeled ``Input.''
png pdf	Table 3: Memory usage with direct inference and the SONIC approach in the local CPU tests at the Purdue Tier-2 cluster. The last column is calculated as the sum of client and server memory usage divided by the direct inference memory usage. To ensure the CPUs are always saturated, the number of threads $ n_\text{T} $ per job multiplied by the number of jobs is set to 20.

Summary

Within the next decade, the data-taking rate at the LHC will increase dramatically, straining the expected computing resources of the LHC experiments. At the same time, more algorithms that run on these resources will be converted into either machine learning or domain algorithms that are easily accelerated with the use of coprocessors, such as graphics processing units (GPUs). By pursuing heterogeneous architectures, it is possible to alleviate potential shortcomings of available central processing unit (CPU) resources. Inference as a service (IaaS) is a promising scheme to integrate coprocessors into CMS computing workflows. In IaaS, client code simply assembles the input data for an algorithm, sends that input to an inference server running either locally or remotely, and retrieves output from the server. The implementation of IaaS discussed throughout this paper is called the Services for Optimized Network Inference on Coprocessors (SONIC) approach, which employs NVIDIA TRITON Inference Servers to host models on coprocessors, as demonstrated here in studies on GPUs, CPUs, and Graphcore Intelligence Processing Units (IPUs). In this paper, the SONIC approach in the CMS software framework ( CMSSW ) is demonstrated in a sample Mini-AOD workflow, where algorithms for jet tagging, tau lepton identification, and missing transverse momentum regression are ported to run on inference servers. These algorithms account for nearly 10% of the total processing time per event in a simulated data set of top quark-antiquark events. After model profiling, which is used to optimize server performance and determine the needed number of GPUs for a given number of client jobs, the expected 10% decrease in per-event processing time was achieved in a large-scale test of Mini-AOD production with the SONIC approach that used about 10\,000 CPU cores and 100 GPUs. The network bandwidth is large enough to support high input-output model inference for the workflow tested, and it will be monitored as the fraction of algorithms using remote GPUs increases. In addition to meeting performance expectations, we demonstrated that the throughput results are not highly sensitive to the physical client-to-server distance, at least up to distances of hundreds of kilometers. Running inference through TRITON servers on local CPU resources does not affect the throughput compared with the standard approach of running inference directly on CPUs in the job thread. We also performed a test using GraphCore IPUs to demonstrate the flexibility of the SONIC approach. The SONIC approach for IaaS represents a flexible method to accelerate algorithms, which is increasingly valuable for LHC experiments. Using a realistic workflow, we highlighted many of the benefits of the SONIC approach, including the use of remote resources, workflow acceleration, and portability to different processor technologies. To make it a viable and robust paradigm for CMS computing in the future, additional studies are ongoing or planned for monitoring and mitigating potential issues such as excessive network and memory usage or server failures.

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