Tessera performs kernel-granularity disaggregation on heterogeneous GPUs, achieving up to 2.3x throughput and 1.6x cost efficiency gains for large model inference while generalizing beyond prior methods.
Canonical reference
Tender: Accelerating large language models via tensor decomposition and runtime requantization,
Canonical reference. 87% of citing Pith papers cite this work as background.
57
Pith papers citing it
Background
87% of classified citations
citation-role summary
background 13
baseline 1
dataset 1
citation-polarity summary
representative citing papers
TCM finds provably optimal DNN accelerator mappings by pruning the search space up to 32 orders of magnitude with a new dataplacement concept, delivering 1.2-6.5x better energy-delay-product in 17 seconds instead of hours.
HMA-Serve enables efficient cross-vendor disaggregated LLM serving on memory-heterogeneous accelerators via phase-wise quantization, compute-transfer pipelining, and deferred dequantization, delivering up to 3.2x goodput and 4.8x goodput-per-dollar.
LENS predicts NPU LLM inference latency with 2.15% mean error by profiling each bucket with two E2E measurements and composing results to capture bucketing non-linearity.
Introduces three linearizable GPU concurrent queues: an adapted wait-free queue using segments, a bounded lock-free queue with wave-batched paths, and a bounded wait-free queue using 64-bit CAS operations.
AVMP separates KV and SSM cache pools behind unified virtual addressing with failure-triggered migration, cutting OOM events 7.6% and raising throughput 1.83-13.3x on synthetic loads and 2.36x on ShareGPT traces.
ITHICA generates functional tests via intra-thread instruction duplication and comparison, detecting 39% more defective servers than baseline methods on over 3000 real CPUs while revealing new defect behaviors.
AtomTwin.jl is a physics-native Julia framework for simulating neutral-atom quantum processors, with a demonstration of logical Bell state preparation using four ytterbium-171 atoms in movable tweezers.
Fleet adds a Chiplet-task level to GPU task models, enabling per-chiplet scheduling and cooperative cache reuse in persistent megakernels, yielding 1.3-1.5x lower LLM decode latency and up to 37% less HBM traffic on AMD MI350 hardware.
KOVAL-Q uses SAT solving to optimize and verify surface-code logical operations with general encodings, finding d-cycle CNOTs and 2d-cycle rotations that reduce FTQC application runtime by about 10 percent.
InfiniLoRA decouples LoRA execution from base-model inference and reports 3.05x higher request throughput plus 54% more adapters meeting strict latency SLOs.
NestPipe achieves up to 3.06x speedup and 94.07% scaling efficiency on 1,536 workers via dual-buffer inter-batch and frozen-window intra-batch pipelining that overlaps communication with computation.
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
PrefixWall mitigates APC side channels in multi-tenant LLM systems via selective prefix isolation, delivering up to 70% higher cache reuse and 30% lower latency than full-isolation baselines.
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
DiLaServe improves SLO attainment for diffusion language models by up to 56.6 percentage points and reduces latency by up to 46% with less than 1% accuracy drop via deadline-aware scheduling and dynamic reconfiguration.
KernelSight-LM simulates token-level LLM inference to predict per-kernel latencies and end-to-end metrics (TTFT, TPOT, throughput) with 12.1% and 3.8% kernel errors in cross-generation and target-measured tiers.
Kamera stores a low-rank patch with each position-free KV chunk to restore cross-chunk conditioning lost in naive reuse, enabling cheap reordering, sliding windows, and recall across attention mechanisms.
A graph-theoretic nonlinear integer program solved via genetic algorithm reduces qubit transfers in neutral atom quantum circuit compilation compared to prior zoned-architecture compilers.
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
CRAM-ER combines spintronic computational RAM with CMOS adder trees and software fine-tuning to deliver near-lossless DNN accuracy at up to 100x lower latency than CPU/GPU baselines.
ThriftAttention recovers 89.1% of the FP16 quality gap versus pure FP4 attention by running only 5% of query-key blocks in FP16 on long-context benchmarks.
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
Develops a simulation framework showing multi-resource stranding changes deployable capacity and effective costs in AI datacenters, arguing the key metric is deployable capacity over time rather than installed megawatts.
citing papers explorer
-
Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
Tessera performs kernel-granularity disaggregation on heterogeneous GPUs, achieving up to 2.3x throughput and 1.6x cost efficiency gains for large model inference while generalizing beyond prior methods.
-
Latency Prediction for LLM Inference on NPU Systems
LENS predicts NPU LLM inference latency with 2.15% mean error by profiling each bucket with two E2E measurements and composing results to capture bucketing non-linearity.
-
Scalable Concurrent Queues for GPU
Introduces three linearizable GPU concurrent queues: an adapted wait-free queue using segments, a bounded lock-free queue with wave-batched paths, and a bounded wait-free queue using 64-bit CAS operations.
-
InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
InfiniLoRA decouples LoRA execution from base-model inference and reports 3.05x higher request throughput plus 54% more adapters meeting strict latency SLOs.
-
NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
NestPipe achieves up to 3.06x speedup and 94.07% scaling efficiency on 1,536 workers via dual-buffer inter-batch and frozen-window intra-batch pipelining that overlaps communication with computation.
-
Kamera: Unified Position-Invariant Multimodal KV Cache for Training-Free Reuse
Kamera stores a low-rank patch with each position-free KV chunk to restore cross-chunk conditioning lost in naive reuse, enabling cheap reordering, sliding windows, and recall across attention mechanisms.
-
Designing Datacenter Power Delivery Hierarchies for the AI Era
Develops a simulation framework showing multi-resource stranding changes deployable capacity and effective costs in AI datacenters, arguing the key metric is deployable capacity over time rather than installed megawatts.
-
ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL
ROSE is a system for cooperative elasticity that co-locates serving and rollout models on shared GPUs, delivering 1.3-3.3x higher end-to-end throughput than fixed-resource baselines while preserving serving SLOs.
-
SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference
SPECTRE achieves up to 2.28x speedup for large-model LLM serving by running speculative draft generation and target verification in parallel using idle tail-model services.
-
Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions
A new taxonomy for dynamics-aware microservice management, synthesized from 84 systems, finds that production dynamics are often only partially modeled and that reported performance gains depend on evaluation realism.
-
LEO: Tracing GPU Stall Root Causes via Cross-Vendor Backward Slicing
LEO performs cross-vendor backward slicing from stalled GPU instructions to attribute root causes to source code, enabling optimizations that produce geometric-mean speedups of 1.73-1.82x on 21 workloads.
-
KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
KAIROS reduces power by 27% on average (up to 39.8%) for agentic AI inference by using long-lived context to jointly manage GPU frequency, concurrency, and request routing across instances.
-
Blink: CPU-Free LLM Inference by Delegating the Serving Stack to GPU and SmartNIC
Blink enables CPU-free LLM inference via SmartNIC offload and persistent GPU kernel, delivering up to 8.47x lower P99 TTFT, 3.4x lower P99 TPOT, 2.1x higher decode throughput, and 48.6% lower energy per token while remaining stable under CPU interference.
-
Performance Isolation and Semantic Determinism in Efficient GPU Spatial Sharing
CoGPU resolves the tradeoff in GPU sharing by introducing GPU coroutines for semantic-preserving resource migration, delivering up to 79.2% higher training throughput and zero token mismatch in inference.
-
DCGen 1.1 Technical Report: Generating Datacenter Configurations (including IT, Power, Cooling)
DCGen generates customizable datacenter configurations with IT, power, and cooling components optimized for power, compute, and area targets using real equipment catalogs and workload-specific IT mixes.
-
KernelFlume: Elastic Core-Attention Scaling for Agentic Long-Context Decoding
KernelFlume presents a disaggregated decode architecture that separates core attention from projection/FFN paths to enable elastic scaling of attention nodes, reporting up to 61% lower cost per million tokens versus full-instance scaling on H100 hardware for Llama-3.1-8B under dynamic long-context w
-
ShuntServe: Cost-Efficient LLM Serving on Heterogeneous Spot GPU Clusters
ShuntServe reports 1.42x and 1.35x higher throughput than baselines plus 31.9 percent and 31.2 percent cost-efficiency gains over on-demand instances for Llama-3.1-70B and Qwen3-32B on heterogeneous AWS spot clusters.
-
Energy-Aware Computing in the Year 2026
The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.