GhOST: A low-overhead GPU out-of-order scheduling technique for reducing instruction stalls

Background

Graphics Processing Units (GPUs) have become essential for accelerating a wide range of parallel workloads, including scientific computing, machine learning, and graphics rendering. Their architecture is designed to exploit massive thread-level parallelism by executing thousands of lightweight threads grouped into warps. However, despite their high throughput capabilities, GPUs often suffer from significant instruction stalls due to data, control, and structural hazards. These stalls are particularly problematic in applications with irregular memory access patterns or variable instruction latencies, where the inability to issue instructions for extended periods can severely limit performance. Traditional approaches to mitigate stalls—such as increasing the number of active warps or optimizing warp scheduling policies—are often insufficient, especially when all warps are blocked by dependencies, highlighting the need for more effective solutions to improve instruction throughput.

Current approaches to reducing instruction stalls in GPUs typically rely on in-order execution within each warp, where only the oldest ready instruction is considered for issue. This rigid scheduling model leads to frequent pipeline stalls when the next instruction is blocked by unresolved dependencies, even if subsequent instructions are independent and ready for execution. While out-of-order (OoO) execution techniques have been highly successful in CPUs for exposing instruction-level parallelism, their adoption in GPUs has been limited by the significant hardware complexity they introduce—such as register renaming, speculative execution, and complex memory disambiguation structures. These features not only increase area and power consumption but also complicate the design and verification of GPU hardware. Moreover, speculative techniques offer limited benefit in GPUs due to the low frequency of control instructions and the high cost of supporting rollback for thousands of threads. As a result, there remains a gap between the potential and realized performance of GPUs, driven by the inefficiency of current in-order scheduling and the impracticality of conventional OoO mechanisms for this domain.

Technology

GhOST is a low-cost, non-speculative out-of-order scheduling technique designed specifically for GPUs to reduce instruction stalls and improve throughput. Implemented primarily in the decode stage, GhOST introduces a Dependence Checker (DC) that identifies data hazards—such as read-after-write, write-after-read, and write-after-write—by comparing incoming instructions to those already present in the Issue Buffer (IsB), which stores multiple instructions per warp along with their dependency metadata. The DC enforces memory instruction ordering and stalls execution for unresolved control instructions, ensuring correctness without speculation. A GhOST Scheduler then selects up to two of the oldest ready instructions per warp from the IsB, based on their dependency status, and places them into an Instruction Table (ITab) for the existing warp scheduler to issue. This approach leverages GPU-specific features, including large register files and interleaved independent instruction chains, while avoiding the need for complex mechanisms like register renaming, speculation, or load-store queues.

What differentiates GhOST is its ability to provide meaningful out-of-order execution benefits at a fraction of the hardware complexity and power overhead associated with traditional CPU-inspired OoO designs. Unlike prior GPU OoO solutions that require extensive hardware modifications—such as register renaming, result broadcasting, and memory reordering—GhOST achieves an average 6% performance improvement (and up to 36% in some cases) with only about 1% additional power consumption and minimal area increase. Its design is tailored to GPU workloads, which typically feature low branch frequencies and multiple independent instruction streams, making speculation and aggressive memory reordering largely unnecessary. By focusing on dynamic stall reduction within the decode stage and maintaining compatibility with existing scheduling policies, GhOST offers a practical, efficient, and easily adoptable solution for enhancing GPU performance without the prohibitive costs of more complex out-of-order architectures.

Benefits

• Reduces GPU instruction stalls by enabling out-of-order scheduling within the decode stage
• Improves GPU throughput by dynamically issuing multiple independent instructions per warp
• Achieves performance gains (average 6%, up to 36%) with minimal hardware overhead and power increase (~1%)
• Avoids complex and costly features like register renaming, speculation, and load-store queues
• Maintains memory instruction ordering and handles control instructions without speculation
• Leverages GPU-specific characteristics such as large register files and low branch frequency for efficient scheduling
• Compatible with existing warp schedulers and other GPU scheduling enhancements without ISA changes

Applications

• High-performance scientific computing
• Real-time medical imaging
• Autonomous vehicle perception systems
• Deep learning model training
• Graph analytics acceleration

Additional info

This GPU scheduling mechanism, GhOST, reduces instruction stalls by dynamically reordering execution. Operating in the decode stage, it employs a Dependence Checker to identify data hazards among instructions in an Issue Buffer. A dedicated scheduler then selects ready instructions, bypassing stalled ones, for the existing warp scheduler. It leverages GPU characteristics, avoiding costly features like speculation or register renaming, for low-overhead performance enhancement.