Hardware transactional memory for GPU architectures
Bibliographic record
Abstract
Graphics processor units (GPUs) are designed to efficiently exploit thread level parallelism (TLP), multiplexing execution of 1000s of concurrent threads on a relatively smaller set of single-instruction, multiple-thread (SIMT) cores to hide various long latency operations. While threads within a CUDA block/OpenCL workgroup can communicate efficiently through an intra-core scratchpad memory, threads in different blocks can only communicate via global memory accesses. Programmers wishing to exploit such communication have to consider data-races that may occur when multiple threads modify the same memory location. Recent GPUs provide a form of inter-block communication through atomic operations for single 32-bit/64-bit words. Although fine-grained locks can be constructed from these atomic operations, synchronization using locks is prone to deadlock. In this paper, we propose to solve these problems by extending GPUs to support transactional memory (TM). Major challenges include supporting 1000s of concurrent transactions and committing non-conflicting transactions in parallel. We propose KILO TM, a novel hardware TM design for GPUs that scales to 1000s of concurrent transactions. Without cache coherency hardware to depend on, it uses word-level, value-based conflict detection to avoid broadcast communication and reduce on-chip storage overhead. It employs speculative validation using a novel bloom filter organization to increase transaction commit parallelism. For a set of TM-enhanced GPU applications, KILO TM captures 59% of the performance of fine-grained locking, and is on average 128x faster than executing all transactions serially, for an estimated hardware area overhead of 0.5% of a commercial GPU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".