GPU Code Optimization Techniques

• GPU Code Optimization is the systematic process of refining code to fully exploit GPU resources by using techniques such as kernel fusion, arithmetic intensity maximization, and automated search.
• Memory hierarchy strategies like staging data in shared memory and packing halo regions reduce global memory traffic, enabling performance gains in applications like PDE solvers and linear algebra routines.
• Automated approaches, including evolutionary computation and LLM-guided tuning, have demonstrated speedups up to 412% while enhancing cross-architecture portability.

GPU code optimization refers to the systematic process of transforming, tuning, and architecting code to maximally exploit the computational and memory resources of modern Graphics Processing Units. This process encompasses kernel fusion, memory hierarchy exploitation, arithmetic intensity enhancement, cross-architecture portability strategies, and automated search-based tuning. Optimization is critical to leverage the extreme throughput and bandwidth of GPUs, with sustained speedups frequently reaching one to two orders of magnitude over baseline CPU or naïve GPU implementations, as demonstrated for high-intensity PDE solvers, linear algebra routines, and large-scale simulations (Shterev, 2018, Filipovič et al., 2013, Sfiligoi et al., 2023, Williams et al., 16 Apr 2024).

1. Kernel Fusion and Arithmetic Intensity

A central principle in GPU optimization is maximizing arithmetic intensity (the ratio of FLOPs to global memory accesses) through kernel fusion. For computational fluid dynamics solvers, fusing all the algorithmic stages of a sweep into as few GPU kernels as possible allows temporaries and intermediate coefficients to remain in registers or local (shared) memory, amortizing the cost of global memory traffic across many operations. This eliminates kernel-launch overhead and enables arithmetic intensities $I = N_\mathrm{FLOP} / N_\mathrm{load}$ reaching $I \gtrsim 1$, and even $I\sim$ 9–20 when all physical terms are included (Shterev, 2018). For linear-algebra pipelines (e.g., BLAS-1/2 sequences), automatic source-to-source kernel-fusion strategies can deliver 1.9–2.6$\times$ speedups by eliminating the need to store or re-load intermediate results to global memory, provided the aggregate register and shared-memory footprint of the fused kernel does not compromise occupancy (Filipovič et al., 2013). The kernel fusion transformation must handle nested map/reduce combinations and insert only local synchronizations without global barriers.

2. Memory Hierarchy and Data Movement Optimization

Minimizing global memory traffic and staging data efficiently through the memory hierarchy is foundational. Temporary per-cell coefficients and arrays are staged in local (shared) memory for tile-based sweeps; cell-centered scalars are held in registers until eviction; only final results are written out (Shterev, 2018). Packing strided faces or halo regions into flat buffers is used in multi-GPU stencil codes to recover contiguous, coalesced memory transactions, reducing device-host transfer cost by up to 2$\times$ (Xue et al., 2020). Libraries such as cuZFP enable transparent on-the-fly compression for out-of-core codes, reducing PCIe bandwidth consumption and overall memory requirements by 33%–55%, effectively shifting the dominant cost back to compute (Shen et al., 2022).

For OpenACC/OpenMP-based codes, porting from NVIDIA to AMD GPUs can expose critical differences in directive handling, vector/gang clause requirements, and data mapping defaults. Streamlining data movement with explicit, persistent data regions and overlapping host-to-device transfers with computation (using async clauses in OpenACC or depend/nowait in OpenMP) is essential for scalable multi-GPU performance, particularly at exascale (Sfiligoi et al., 2023, Williams et al., 16 Apr 2024).

3. Instruction-Level Parallelism, Redundancy Elimination, and Computational Reordering

Modern optimizers apply common-subexpression elimination (CSE), bulk memory-load hoisting, and algebraic reordering to reduce both instruction and memory pressure. Frameworks based on equality saturation operate on SSA-form representations of kernel bodies, constructing e-graphs to exhaustively encode all algebraically equivalent rewrites. Extracting globally optimal variants from this space (through cost-minimization and register-aware scheduling) allows for full FMA-fusion, arithmetic/operator reordering, and bulk-load hoisting for maximum coalescence (Matsumura et al., 2023). On memory-bound kernels, these techniques typically bring 1.5–2$\times$ throughput improvement; on compute-bound kernels, they reduce register pressure and dynamic instruction count.

High-intensity kernels avoid expensive operations (division, sqrt, pow) via algebraic reformulations or use of fast-math intrinsics (e.g., replacing $\exp$ with $\_\_expf$), and exploit warp-synchronous primitives to minimize divergence and barrier overhead, as exemplified by state-of-the-art Gaussian splatting pipelines (Hu et al., 30 Sep 2025).

4. Search-Driven and Automated Evolutionary Optimization

Recent advances leverage large-scale evolutionary computation and LLM-driven frameworks to explore the vast combinatorial space of possible GPU kernel variants. Systems such as GEVO operate on LLVM-IR, evolving mutated candidate programs under fitness functions that balance runtime and correctness (including approximate error when desired). Originally demonstrated on the Rodinia suite and scientific simulation kernels, evolutionary search discovers non-obvious compound improvements and interdependent edit clusters, routinely achieving 30–50% speedups over hand-tuned baselines—with maxima over 3$\times$ in favorable cases. Epistatic effects are prevalent: performance gains often require multiple coordinated code mutations not evident in isolation (Liou et al., 2020, Liou et al., 2022).

LLM-powered workflows, exemplified by GPU Kernel Scientist, further automate the planning, edit-proposal, and variant selection loop. LLMs interpret both prior code and timing vectors, contextually generate multiple hypotheses, synthesize kernel code, and select promising variants according to predicted impact. This paradigm enables high performance on under-documented or rapidly evolving hardware, with speedups of $>$10$\times$ over naïve ports and nearly closing the gap with top human experts in contests on new architectures (Andrews et al., 25 Jun 2025).

5. Practical Best Practices for Diverse Application Domains

Table: Representative Best Practices in GPU Code Optimization

Strategy	Description	Empirical Impact
Kernel fusion	Merge dependent phases into as few kernels as architecture permits	2$\times$–20$\times$ speedup
Local/private memory use	Stage temporaries in shared memory/registers	Cuts global traffic by $>$50%
Arithmetic intensity maximization	Reuse loaded data via large expressions or tile-based computation	Approaches roofline bound
Explicit unrolling and reordering	Unroll loops, reorder computations for FMA and register use	$>$1.2$\times$ speedup
Asynchronous, batch-overlap programming	Overlap data transfers and compute across streams/GPUs	Maintains $>$50% scaling
Profile-driven bottleneck analysis	Use tools (Nsight, rocprof) to locate hot spots and tune accordingly	Reveals dominant stalls
Evolutionary/LLM-based search	Iterative black-box or LLM-guided variant generation and selection	Up to 412% in best cases

Specific practices must preserve or enhance occupancy: fusing too many operations or allocating excessive shared/register memory can reduce the number of active warps per SM, reducing throughput. Memory layout must preserve coalesced access; struct-of-arrays transforms, and halo/face-packing are frequently required for structured scientific applications (Jeong et al., 2015, Chen et al., 2018, Wang et al., 2021).

6. Auto-Tuning, Search Spaces, and Optimization Algorithm Selection

Given the highly nonconvex, discrete nature of kernel parameter spaces (block sizes, tile sizes, unroll factors, memory tiling, etc.), auto-tuning presents as a black-box search over a combinatorial landscape. State-of-the-art evaluations benchmark 16 optimization algorithms, finding that continuous-space methods (dual annealing) excel for small evaluation budgets ($<200$), while iterated local search, multi-start local search, and hybrid evolutionary methods dominate at moderate to high budgets (Schoonhoven et al., 2022). A PageRank-derived centrality metric on the kernel's neighborhood graph can accurately predict the difficulty of achieving near-optimal configurations.

7. Limitations, Portability, and Cross-Architectural Considerations

Portability between GPU vendors (NVIDIA, AMD) and architectures (Kepler, Maxwell, Ampere, MI200/MI300) imposes challenges due to differences in register files, L1/L2 cache hierarchy, and compiler directive implementation. Optimization strategies relying on architecture-specific behavior (e.g., warp shuffle instructions, occupancy-critical fusions) require explicit validation and sometimes conditional code paths (Sfiligoi et al., 2023). Recent research emphasizes that optimizations discovered for one architecture may transfer (sometimes with diminished returns) to others, but rigorous profile-driven empirical testing on all targets remains necessary for performance portability.

• GPU implementation of algorithm SIMPLE-TS for calculation of unsteady, viscous, compressible and heat-conductive gas flows (Shterev, 2018)
• Optimizing CUDA Code By Kernel Fusion---Application on BLAS (Filipovič et al., 2013)
• Optimization and Portability of a Fusion OpenACC-based FORTRAN HPC Code from NVIDIA to AMD GPUs (Sfiligoi et al., 2023)
• ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code (Matsumura et al., 2023)
• LLM-Powered Code Analysis and Optimization for Gaussian Splatting Kernels (Hu et al., 30 Sep 2025)
• A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels (Taheri et al., 2019)
• GEVO: GPU Code Optimization using Evolutionary Computation (Liou et al., 2020)
• Understanding the Power of Evolutionary Computation for GPU Code Optimization (Liou et al., 2022)
• GPU Kernel Scientist: An LLM-Driven Framework for Iterative Kernel Optimization (Andrews et al., 25 Jun 2025)
• Benchmarking optimization algorithms for auto-tuning GPU kernels (Schoonhoven et al., 2022)
• Compression-Based Optimizations for Out-of-Core GPU Stencil Computation (Shen et al., 2022)
• Performance of GTX Titan X GPUs and Code Optimization (Jeong et al., 2015)
• Comprehensive Optimization of Parametric Kernels for Graphics Processing Units (Chen et al., 2018)
• PhotoNs-GPU:A GPU accelerated cosmological simulation code (Wang et al., 2021)
• Bonsai: A GPU Tree-Code (Bédorf et al., 2012)