AI Workload Optimization
XTC: Unifying AI Operator Scheduling for Enterprise Performance
Achieving high efficiency on AI operators demands precise control over computation and data movement. Existing scheduling languages are often locked into specific compiler ecosystems, hindering comparison, reuse, and evaluation across frameworks. XTC provides a unified platform with a common API and reproducible measurement framework, enabling portable experimentation and accelerating research on advanced optimization strategies.
Unlock Unprecedented AI Efficiency
XTC directly addresses critical enterprise needs for AI workload optimization, delivering measurable impacts across performance, research, and development cycles.
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Speedup Potential (Intel)
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Reproducible Measurements
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Accelerated AI Research
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Unified API & Scheduling
Infrastructure & Measurement
Research & Evaluation
Unified API & Scheduling Innovation
XTC revolutionizes AI operator optimization by decoupling scheduling from code generation, fostering focused research and enabling seamless integration across diverse compiler frameworks. This unified approach simplifies experimentation and allows for deeper insights into performance.
- ✓ Decouples scheduling from code generation, enabling focused research on optimization strategies.
- ✓ Introduces a unified API abstracting core components from multiple scheduling languages (TVM, MLIR).
- ✓ Exposes 10 core scheduling primitives: strip mine, interchange, unroll, vectorize, parallelize, split, pack, bufferize, fuse.
- ✓ Offers a higher-level declarative scheduling language for simplified manual experimentation and improved reasoning.
Enterprise Process Flow: XTC's Component Interaction
Driver (autotuner or human expert)
→
Unified scheduling language (§ 3)
→
Code generator backends (§ 4.1)
→
On-chip validation and measurement harness (§ 4.2)
| XTC Primitive | TVM/TE Counterpart | MLIR Transform Dialect Counterpart |
|---|---|---|
| Strip mine | split | tile_using_for (1D) |
| Interchang | reorder | Implicitly carried by the dataflow of the script |
| Unroll | unroll | loop.unroll |
| Vectorize | vectorize | vectorize + apply_patterns |
| Parallelize | fuse + parallel | tile_using_forall (1D) |
| Split | loop_partition | split_handle + split |
| Pack | cache_read + compute_at | pack |
| Bufferize | cache_write + compute_at | pack |
| Fuse | compute_at | fuse_into_containing_op |
Robust Infrastructure & Performance Metrics
XTC's architecture provides a powerful foundation for AI optimization research, integrating seamlessly with existing compilation frameworks and offering advanced measurement capabilities to ensure reproducibility and accuracy across diverse hardware.
- ✓ Integrates with state-of-the-art backends like TVM and MLIR Transform dialect, leveraging their rapidly evolving infrastructure.
- ✓ Provides a cross-platform measurement harness for detailed hardware performance metrics, including CPU counters (libpfm4, KPerf) and NVIDIA GPU profiling (CUpti).
- ✓ Ensures reproducible and quantitative comparisons across various compilation pipelines and hardware stacks (x86, ARM, NVIDIA GPUs).
- ✓ Supports automated design space exploration, allowing experts to connect high-level strategies with custom sampling and predictive models.
Advancing AI Optimization Research
XTC serves as a vital research platform, enabling detailed analysis, validation of performance models, and seamless integration into complex AI pipelines, driving both innovation and practical efficiency gains.
- ✓ Enables fair comparison, reproducible measurement, and rapid prototyping of optimization strategies.
- ✓ Demonstrates performance comparable to hand-tuned C code with vector intrinsics.
- ✓ Reveals backend limitations and allows for performance model evaluation, such as L1 cache misses correlation on Apple M4 Max.
- ✓ Integrates seamlessly into complete inference pipelines (e.g., Aidge framework) for mixed C++ templates and compiled subgraphs.
- ✓ Achieves significant speedups (x15-x30 on Intel, x2-x4 on ARM) within integrated deep learning frameworks.
30x
Peak Speedup on Intel CPUs with XTC
Aidge Framework Integration: Real-world Impact
XTC seamlessly integrates with the Aidge framework, enabling mixed generation of C++ templates and compiled neural network subgraphs. This approach compiles selected subgraphs for optimization, yielding significant speedups on Intel (x15-x30) and ARM (x2-x4) machines, demonstrating the platform's versatility in real-world AI inference pipelines. This proves XTC's ability to drive substantial performance gains for enterprise-grade AI applications.
Quantify Your Enterprise AI Advantage
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Annual Cost Savings
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Our Proven Implementation Roadmap
Leverage XTC to streamline your AI workload optimization with a structured, efficient, and results-driven approach.
Discovery & Strategy
Initial consultation to align AI optimization goals with your overarching business objectives and current infrastructure.
Platform Integration
Seamless integration of XTC within your existing compiler frameworks (TVM, MLIR, or custom backends) and development pipelines.
Optimization & Tuning
Apply advanced scheduling strategies and utilize XTC's reproducible measurement framework to fine-tune AI workload performance.
Deployment & Scaling
Roll out optimized AI operators across your target hardware, ensuring maximum efficiency, portability, and sustained impact.
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