Modern computing, especially within large data centers, faces a significant challenge: processors often struggle to keep up with the massive workloads due to the difficulty in predicting and preparing upcoming instructions. This bottleneck slows down data flow and impacts everything from search engine response times to overall computing efficiency.
In response to this challenge, a novel technique named "Skia," a Greek word for shadow, has been developed by researchers at Texas A&M University in collaboration with Intel, AheadComputing, and Princeton. Skia aims to enhance CPU branch prediction, a critical process where the processor attempts to guess the outcome of a conditional instruction (like an "if" statement) before it's actually executed. Accurate branch prediction is essential for keeping the processor's instruction pipeline full and running efficiently, thereby improving performance and reducing power consumption.
The core problem Skia addresses is the high rate of Branch Target Buffer (BTB) misses. The BTB is a small, fast memory that stores the target addresses of recently executed branch instructions. When a branch instruction is encountered, the BTB is checked. If the branch is found (a BTB hit), the processor can quickly fetch instructions from the predicted target. However, if the branch is not in the BTB (a BTB miss), the processor may have to stall while the branch outcome is determined, leading to performance degradation. This is particularly problematic for modern data center workloads where complex applications have large code footprints, leading to frequent BTB evictions and subsequent misses.
Skia introduces the concept of "Shadow Branches." These are branch instructions that are already present in the instruction cache (L1-I cache) lines that have been previously fetched by a mechanism like Fetch Directed Instruction Prefetching (FDIP), but have not yet been decoded and stored in the BTB. This often happens because they fall outside the currently executing basic block of instructions. Skia observes that a significant majority—around 75%—of BTB-missing, unidentified branches are, in fact, these shadow branches.
The Skia technique works by identifying and decoding these shadow branches from the unused bytes within the fetched cache lines. These decoded shadow branches are then stored in a dedicated memory area called the Shadow Branch Buffer (SBB). Crucially, the SBB is accessed in parallel with the main BTB. This allows the FDIP mechanism to continue speculating and prefetching instructions even when a BTB miss occurs, by consulting the SBB.
This approach offers several advantages. It utilizes information already present in the instruction cache, meaning it doesn't require additional, costly accesses to the instruction cache. Furthermore, it avoids polluting the critical path of the processor's operation. By making these previously hidden ("shadow") branches visible and usable, Skia effectively increases the processor's ability to foresee future instructions.
Researchers have demonstrated that Skia can achieve significant performance improvements with a minimal hardware budget. For instance, with a relatively small storage state of 12.25KB, Skia has shown a geomean speedup of approximately 5.7% over an 8K-entry BTB (which itself occupies around 78KB). This performance gain is about 2% better than simply adding an equivalent amount of storage to the existing BTB. The effectiveness of Skia stems from the fact that many branches stored in the SBB are distinct from those in a similarly sized BTB, leading to consistently greater performance gains across various configurations until the point of saturation. The technique has shown particular effectiveness for tail branches (branches at the end of a cache line) due to the reduced complexity in determining valid instructions in those blocks.
The development of Skia represents a valuable advancement in processor front-end architecture. By shedding light on and leveraging these "shadow branches," the technique offers a pathway to more efficient data centers, quicker performance, and reduced power consumption. It addresses a key bottleneck in modern processor design, paving the way for hardware better suited to the demands of complex, large-footprint applications.
Beyond Skia, the field of branch prediction continues to evolve. Researchers are exploring various avenues, including:
- Deep Learning and Machine Learning: There's growing interest in applying deep learning (DL) and machine learning (ML) techniques to create more dynamic and accurate branch predictors. Models like convolutional neural networks (CNNs) and transformer models (such as TinyBERT) are being investigated for their potential to identify complex patterns in branch behavior that traditional algorithms might miss. These ML-based predictors can learn from vast amounts of data and adapt their prediction strategies. Some approaches involve offline training of models, while others explore hybrid systems combining traditional predictors with ML components.
- Advanced Predictor Architectures: Innovations like the Last-Level Branch Predictor (LLBP) aim to improve accuracy by using larger, secondary storage to back up the primary in-core predictor, leveraging program context (like call chains) to prefetch relevant branch metadata.
- Profile-Guided Optimizations: Compilers can perform profile-guided optimizations to rearrange code, maximizing instruction cache locality and branch prediction efficiency along frequently executed code paths.
- Ahead Prediction: This technique aims to mitigate the latency of complex branch predictors by predicting further ahead in the instruction stream. Research in this area focuses on making ahead prediction practical by managing the associated energy costs.
- Two-Level Prediction Mechanisms: Optimizing the Branch Target Buffer (BTB) itself remains a focus, with research into multi-level BTB structures to balance performance, power consumption, and hit rates.
In conclusion, while the "Skia" technique offers a specific and promising solution to improve CPU branch prediction by exposing and utilizing "shadow branches," it is part of a broader, ongoing effort to enhance computing performance through more intelligent and efficient instruction handling. As workloads become increasingly complex, such advancements in CPU architecture are critical for meeting the demands of modern computing.