A new heuristic compiler for multi-qubit iceberg patches reduces circuit depth by 34 percent, cuts gate counts, and improves fidelity metrics on 71 benchmarks compared with naive mapping.
In2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
Simulation study shows cold TLB misses in reverse address translation dominate latency for small collectives in multi-GPU pods, causing up to 1.4x degradation, while larger ones see diminishing returns.
ELMoE-3D achieves 6.6x average speedup and 4.4x energy efficiency gain for MoE serving on 3D hardware by scaling expert and bit elasticity for elastic self-speculative decoding.
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
Duon eliminates TLB shootdown and cache invalidation costs during page migration in flat-address hybrid memory systems by updating mappings in-place, delivering 3.87% IPC gains over prior methods.
citing papers explorer
-
Logical Compilation for Multi-Qubit Iceberg Patches
A new heuristic compiler for multi-qubit iceberg patches reduces circuit depth by 34 percent, cuts gate counts, and improves fidelity metrics on 71 benchmarks compared with naive mapping.
-
Analyzing Reverse Address Translation Overheads in Multi-GPU Scale-Up Pods
Simulation study shows cold TLB misses in reverse address translation dominate latency for small collectives in multi-GPU pods, causing up to 1.4x degradation, while larger ones see diminishing returns.
-
ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving
ELMoE-3D achieves 6.6x average speedup and 4.4x energy efficiency gain for MoE serving on 3D hardware by scaling expert and bit elasticity for elastic self-speculative decoding.
-
The Energy Cost of Execution-Idle in GPU Clusters
Execution-idle accounts for 19.7% of GPU execution time and 10.7% of energy in a large cluster, motivating power management that treats it as a distinct operating state.
-
AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems
AEGIS reduces inter-GPU communication by up to 81.3% in self-attention and reaches 96.62% scaling efficiency with 3.86x speedup on four GPUs for 2048-token encrypted Transformer inference.
-
Efficient Page Migration in Hybrid Memory Systems
Duon eliminates TLB shootdown and cache invalidation costs during page migration in flat-address hybrid memory systems by updating mappings in-place, delivering 3.87% IPC gains over prior methods.