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Software Frameworks Optimized for GPUs in AI: CUDA, ROCm, Triton, TensorRT—Compiler Paths and Performance Implications

ByRicardo

Table of contents

Deep-learning throughput hinges on how successfully a compiler stack maps tensor applications to GPU execution: thread/block schedules, reminiscence motion, and instruction choice (e.g., Tensor Core MMA pipelines). In this text we are going to concentrate on 4 dominant stacks—CUDA, ROCm, Triton, and TensorRT—from the compiler’s perspective and explains which optimizations transfer the needle in observe.

What really determines efficiency on fashionable GPUs

Across distributors, the identical levers recur:

  • Operator scheduling & fusion: cut back kernel launches and round-trips to HBM; expose longer producer→client chains for register/shared-memory reuse. TensorRT and cuDNN “runtime fusion engines” exemplify this for consideration and conv blocks.
  • Tiling & knowledge structure: match tile shapes to Tensor Core/WGMMA/WMMA native fragment sizes; keep away from shared-memory financial institution conflicts and partition tenting. CUTLASS paperwork warp-level GEMM tiling for each Tensor Cores and CUDA cores.
  • Precision & quantization: FP16/BF16/FP8 for coaching/inference; INT8/INT4 (calibrated or QAT) for inference. TensorRT automates calibration and kernel choice underneath these precisions.
  • Graph seize & runtime specialization: graph execution to amortize launch overheads; dynamic fusion of widespread subgraphs (e.g., consideration). cuDNN 9 added graph assist for consideration fusion engines.
  • Autotuning: search tile sizes, unroll elements, and pipelining depths per arch/SKU. Triton and CUTLASS expose express autotune hooks; TensorRT performs builder-time tactic choice.

With that lens, right here’s how every stack implements the above.

CUDA: nvcc/ptxas, cuDNN, CUTLASS, and CUDA Graphs

Compiler path. CUDA code compiles by way of nvcc into PTX, then ptxas lowers PTX to SASS (arch-specific machine code). Controlling optimization requires feeding flags to each host and gadget phases; for kernels the secret is -Xptxas. Developers typically miss that -O3 alone impacts solely host code.

Kernel technology & libraries.

  • CUTLASS gives parametric templates for GEMM/conv, implementing warp-level tiling, Tensor Core MMA pipelines, and smem iterators designed for conflict-free entry—canonical references for writing peak kernels, together with Hopper’s WGMMA path.
  • cuDNN 9 launched runtime fusion engines (notably for consideration blocks), native CUDA Graph integration for these engines, and updates for new compute capabilities—materially decreasing dispatch overheads and bettering reminiscence locality in Transformer workloads.

Performance implications.

  • Moving from unfused PyTorch ops to cuDNN consideration fusion usually cuts kernel launches and international reminiscence site visitors; mixed with CUDA Graphs, it reduces CPU bottlenecks in short-sequence inference.
  • On Hopper/Blackwell, aligning tile shapes to WGMMA/Tensor Core native sizes is decisive; CUTLASS tutorials quantify how mis-sized tiles waste tensor-core throughput.

When CUDA is the correct instrument. You want most management over instruction choice, occupancy, and smem choreography; otherwise you’re extending kernels past library protection whereas staying on NVIDIA GPUs.

ROCm: HIP/Clang toolchain, rocBLAS/MIOpen, and the 6.x sequence

Compiler path. ROCm makes use of Clang/LLVM to compile HIP (CUDA-like) into GCN/RDNA ISA. The 6.x sequence has centered on perf and framework protection; launch notes observe component-level optimizations and HW/OS assist.

Libraries and kernels.

  • rocBLAS and MIOpen implement GEMM/conv primitives with arch-aware tiling and algorithm choice comparable in spirit to cuBLAS/cuDNN. The consolidated changelog highlights iterative perf work throughout these libraries.
  • Recent ROCm workstream consists of higher Triton enablement on AMD GPUs, enabling Python-level kernel authoring whereas nonetheless decreasing by way of LLVM to AMD backends.

Performance implications.

  • On AMD GPUs, matching LDS (shared reminiscence) financial institution widths and vectorized international hundreds to matrix tile shapes is as pivotal as smem financial institution alignment on NVIDIA. Compiler-assisted fusion in frameworks (e.g., consideration) plus library autotuning in rocBLAS/MIOpen usually closes a big fraction of the hole to handwritten kernels, contingent on structure/driver. Release documentation signifies steady tuner enhancements in 6.0–6.4.x.

When ROCm is the correct instrument. You want native assist and optimization on AMD accelerators, with HIP portability from present CUDA-style kernels and a transparent LLVM toolchain.

Triton: a DSL and compiler for customized kernels

Compiler path. Triton is a Python-embedded DSL that lowers through LLVM; it handles vectorization, reminiscence coalescing, and register allocation whereas giving express management over block sizes and program IDs. Build docs present the LLVM dependency and customized builds; NVIDIA’s developer supplies talk about Triton’s tuning for newer architectures (e.g., Blackwell) with FP16/FP8 GEMM enhancements.

Optimizations.

  • Autotuning over tile sizes, num_warps, and pipelining levels; static masking for boundary situations with out scalar fallbacks; shared-memory staging and software program pipelining to overlap international hundreds with compute.
  • Triton’s design goals to automate the error-prone components of CUDA-level optimization whereas leaving block-level tiling decisions to the creator; the unique announcement outlines that separation of considerations.

Performance implications.

  • Triton shines while you want a fused, shape-specialized kernel exterior library protection (e.g., bespoke consideration variants, normalization-activation-matmul chains). On fashionable NVIDIA components, vendor collabs report architecture-specific enhancements in the Triton backend, decreasing the penalty versus CUTLASS-style kernels for widespread GEMMs.

When Triton is the correct instrument. You need near-CUDA efficiency for customized fused ops with out writing SASS/WMMA, and you worth Python-first iteration with autotuning.

TensorRT (and TensorRT-LLM): builder-time graph optimization for inference

Compiler path. TensorRT ingests ONNX or framework graphs and emits a hardware-specific engine. During the construct, it performs layer/tensor fusion, precision calibration (INT8, FP8/FP16), and kernel tactic choice; best-practice docs describe these builder phases. TensorRT-LLM extends this with LLM-specific runtime optimizations.

Optimizations.

  • Graph-level: fixed folding, concat-slice canonicalization, conv-bias-activation fusion, consideration fusion.
  • Precision: post-training calibration (entropy/percentile/mse) and per-tensor quantization, plus smooth-quant/QAT workflows in TensorRT-LLM.
  • Runtime: paged-KV cache, in-flight batching, and scheduling for multi-stream/multi-GPU deployments (TensorRT-LLM docs).

Performance implications.

  • The largest wins usually come from: end-to-end INT8 (or FP8 on Hopper/Blackwell the place supported), eradicating framework overhead through a single engine, and aggressive consideration fusion. TensorRT’s builder produces per-arch engine plans to keep away from generic kernels at runtime.

When TensorRT is the correct instrument. Production inference on NVIDIA GPUs the place you’ll be able to pre-compile an optimized engine and profit from quantization and large-graph fusion.

Practical steering: selecting and tuning the stack

  1. Training vs. inference.
    • Training/experimental kernels → CUDA + CUTLASS (NVIDIA) or ROCm + rocBLAS/MIOpen (AMD); Triton for customized fused ops.
    • Production inference on NVIDIA → TensorRT/TensorRT-LLM for international graph-level positive factors.
  2. Exploit architecture-native directions.
    • On NVIDIA Hopper/Blackwell, guarantee tiles map to WGMMA/WMMA sizes; CUTLASS supplies present how warp-level GEMM and smem iterators ought to be structured.
    • On AMD, align LDS utilization and vector widths to CU datapaths; leverage ROCm 6.x autotuners and Triton-on-ROCm for shape-specialized ops.
  3. Fuse first, then quantize.
    • Kernel/graph fusion reduces reminiscence site visitors; quantization reduces bandwidth and will increase math density. TensorRT’s builder-time fusions plus INT8/FP8 typically ship multiplicative positive factors.
  4. Use graph execution for quick sequences.
    • CUDA Graphs built-in with cuDNN consideration fusions amortize launch overheads in autoregressive inference.
  5. Treat compiler flags as first-class.
    • For CUDA, keep in mind device-side flags: instance, -Xptxas -O3,-v (and -Xptxas -O0 when diagnosing). Host-only -O3 isn’t ample.

References:

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