Implementation Reference

J.1 EMA Scoring Implementation

class EMAScorer:
    def __init__(self, num_positions, num_heads, alpha=0.1):
        self.alpha = alpha
        self.scores = np.zeros((num_positions, num_heads))
    
    def update(self, attention_weights):
        # attention_weights: [num_heads, num_positions]
        self.scores = (
            self.alpha * attention_weights.T + 
            (1 - self.alpha) * self.scores
        )
    
    def get_eviction_candidates(self, n, anchor_zone=100):
        # Aggregate across heads (max)
        agg_scores = self.scores.max(axis=1)
        # Protect anchor zone
        agg_scores[:anchor_zone] = float('inf')
        # Return n lowest-scoring positions
        return np.argsort(agg_scores)[:n]

J.2 Prefetch Priority

def compute_prefetch_priority(current_pos, cache_positions, ema_scores):
    priorities = []
    for pos in cache_positions:
        distance = abs(current_pos - pos)
        rope_factor = 1.0 / (1 + distance / 100)  # RoPE decay
        ema_factor = ema_scores[pos].max()
        priority = 0.6 * rope_factor + 0.4 * ema_factor
        priorities.append((pos, priority))
    return sorted(priorities, key=lambda x: -x[1])

J.3 Configuration Parameters

J.4 vLLM Integration

from vllm import LLM
from endpoint_cache import EndpointCache

cache = EndpointCache(
    endpoints=["cxl://ep0", "cxl://ep1", "cxl://ep2", "cxl://ep3"],
    policy="ema_rope",
    config={"ema_alpha": 0.1, "anchor_zone": 100}
)

llm = LLM(
    model="meta-llama/Llama-2-70b",
    kv_cache_backend=cache,
    max_model_len=128000,
    gpu_memory_utilization=0.9
)