Optimizing Profit and Loss Modeling with ISO C++ Parallelism on GPUs
Ted Hisokawa Aug 07, 2024 15:37
NVIDIA explores profit and loss modeling using ISO C++ parallelism on GPUs, enhancing performance and efficiency in quantitative finance applications.
In a recent blog post, NVIDIA delves into the intricacies of optimizing profit and loss (P&L) modeling using ISO C++ parallelism on GPUs. This approach aims to enhance performance and efficiency in quantitative finance applications.
Profit and Loss Modeling Explained
Profit and loss modeling is a pivotal aspect of trading strategies, particularly those involving delta-hedging an option position. Under Black-Scholes assumptions, if an investor successfully hedges away the underlying risk, the primary contributor to P&L is the difference between the squares of the realized volatility and the volatility used to price and hedge the option.
Estimating a full P&L distribution for a large portfolio of options can be compute-intensive, necessitating the extension of parallel Black-Scholes code. This involves simulating paths of the underlying asset and accumulating P&L along those paths.
Parallel P&L Simulations
NVIDIA's approach leverages ISO C++ standard parallelism to run applications in parallel on modern multicore CPUs or GPUs without modification. The baseline code from a previous example serves as a starting point, where each path is looped over and P&L calculations are parallelized over options.
Figure 2 in the post illustrates a heatmap of the options grid and four simulated paths. Each grid cell represents an option contract with its corresponding moneyness and time to maturity. The color in the heatmap is proportional to the average P&L across these paths.
Increasing Parallelism for Performance
When parallel algorithms are offloaded to a GPU, launch latency and synchronization overheads are introduced. Although these overheads are minimal, they accumulate when done repeatedly. The NVIDIA Nsight Systems profiler reveals that each kernel requires a device synchronization step longer than the kernel itself.
To address this, NVIDIA suggests parallelizing across paths, ensuring no two paths update the same memory location simultaneously. This is achieved using C++ atomic_ref to prevent race conditions. By parallelizing over both paths and options, the approach minimizes GPU-CPU interactions, launching a single operation on the GPU for the complete dataset and waiting for the result one time.
Figure 3 demonstrates the significant performance improvement realized by optimizing the GPU code to reduce launch and synchronization overheads, exposing more parallelism.
Explore the Code
The acceleration achieved in this quantitative finance example using the code in the NVIDIA/accelerated-quant-finance GitHub repository can be applied to other C++ applications. Any C++ code with serial loops can be modified using standard language parallelism to achieve significant GPU acceleration.
To produce portable and parallel-first code, download the NVIDIA HPC SDK, which contains tools to utilize ISO C++ standard parallelism and profile the results.
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