feat(CCCL): LD_PRELOAD CachingDeviceAllocator — intercept cudaMalloc/cudaFree

Route C: replace PyTorch's cudaMalloc/cudaFree with CCCL CUB's
CachingDeviceAllocator via LD_PRELOAD. Eliminates driver-level allocation
overhead by reusing freed GPU memory from a bin-based cache.

Based on cccl_upstream/cub/cub/util_allocator.cuh (901 lines).
Self-contained .so with no CCCL header dependencies at compile time.

Files:
- cccl_preload_allocator.cu: the allocator (405 lines)
- build_cccl_preload_allocator.sh: build script (corex clang++ or g++ fallback)
- test_cccl_preload.sh: smoke test suite for BI-V100
- patch_ops.sh: build during docker build
- computility-run.yaml: LD_PRELOAD env var for runtime

Config via env:
  CCCL_ALLOC_BIN_GROWTH=8, MIN_BIN=3, MAX_BIN=13, MAX_CACHED_MB=4096

Test on real machine:
  cd qwen3_6_scripts && bash test_cccl_preload.sh

qwen3_6_scripts/test_cccl_preload.sh

@@ -0,0 +1,121 @@
+#!/usr/bin/env bash
+# Quick test for CCCL preload allocator on BI-V100
+# Usage: cd /home/dylan/project_6/qwen3_6_scripts && bash test_cccl_preload.sh
+set -eo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SO="${SCRIPT_DIR}/cccl_preload_allocator.so"
+
+echo "=== Step 1: Build ==="
+bash "${SCRIPT_DIR}/build_cccl_preload_allocator.sh" "${SCRIPT_DIR}"
+echo ""
+
+if [[ ! -f "$SO" ]]; then
+    echo "BUILD FAILED: $SO not found"
+    exit 1
+fi
+
+echo "=== Step 2: Basic smoke test (torch.zeros on GPU) ==="
+echo "Without preload:"
+python3 -c "
+import time, torch
+t0=time.time()
+for i in range(100):
+    x=torch.zeros(1024*1024, device='cuda')
+    del x
+    torch.cuda.synchronize()
+print(f'100 alloc+free cycles: {time.time()-t0:.3f}s')
+"
+
+echo ""
+echo "With CCCL preload:"
+CCCL_ALLOC_DEBUG=0 LD_PRELOAD="$SO" python3 -c "
+import time, torch
+t0=time.time()
+for i in range(100):
+    x=torch.zeros(1024*1024, device='cuda')
+    del x
+    torch.cuda.synchronize()
+print(f'100 alloc+free cycles: {time.time()-t0:.3f}s')
+" 2>&1
+
+echo ""
+echo "=== Step 3: Varied sizes (simulating model inference allocations) ==="
+CCCL_ALLOC_DEBUG=0 LD_PRELOAD="$SO" python3 -c "
+import time, torch
+
+# Simulate inference: repeated allocs of same sizes (should hit cache)
+sizes = [512, 4096, 32768, 262144, 1048576, 4194304, 16777216]  # 512B to 16MB
+tensors = []
+
+print('First pass (cold cache):')
+t0 = time.time()
+for s in sizes:
+    x = torch.empty(s // 2, dtype=torch.float16, device='cuda')  # s bytes
+    tensors.append(x)
+t1 = time.time()
+print(f'  {len(sizes)} allocs: {(t1-t0)*1000:.1f}ms')
+
+print('Free all:')
+del tensors
+torch.cuda.synchronize()
+t2 = time.time()
+print(f'  {len(sizes)} frees: {(t2-t1)*1000:.1f}ms')
+
+print('Second pass (warm cache - should be faster):')
+tensors2 = []
+for s in sizes:
+    x = torch.empty(s // 2, dtype=torch.float16, device='cuda')
+    tensors2.append(x)
+t3 = time.time()
+print(f'  {len(sizes)} allocs: {(t3-t2)*1000:.1f}ms')
+
+print('Third pass (reuse same sizes 100x):')
+for _ in range(100):
+    for s in sizes:
+        x = torch.empty(s // 2, dtype=torch.float16, device='cuda')
+        del x
+t4 = time.time()
+print(f'  700 alloc+free: {(t4-t3)*1000:.1f}ms ({(t4-t3)/700*1000000:.0f}μs/op)')
+" 2>&1
+
+echo ""
+echo "=== Step 4: Large allocation test (model weights sized) ==="
+CCCL_ALLOC_DEBUG=0 LD_PRELOAD="$SO" python3 -c "
+import torch
+# Simulate KV cache blocks (typical: 256KB-2MB each)
+blocks = []
+for i in range(100):
+    b = torch.empty(256*1024 // 2, dtype=torch.float16, device='cuda')
+    blocks.append(b)
+print(f'Allocated 100 x 256KB blocks = {100*256/1024:.0f}MB')
+del blocks
+torch.cuda.synchronize()
+print('Freed all blocks')
+# Reallocate (should hit cache)
+blocks2 = []
+for i in range(100):
+    b = torch.empty(256*1024 // 2, dtype=torch.float16, device='cuda')
+    blocks2.append(b)
+print('Re-allocated 100 blocks (from cache)')
+print('OK: large allocation test passed')
+" 2>&1
+
+echo ""
+echo "=== Step 5: Stats output ==="
+CCCL_ALLOC_DEBUG=0 LD_PRELOAD="$SO" python3 -c "
+import torch
+for _ in range(50):
+    x = torch.zeros(1024*1024, device='cuda')
+    del x
+# Stats print on process exit
+" 2>&1
+
+echo ""
+echo "=== DONE ==="
+echo "If all tests passed, add to your launch command:"
+echo "  LD_PRELOAD=$SO python3 -m vllm.entrypoints.openai.api_server ..."
+echo ""
+echo "Or set in computility-run.yaml env:"
+echo "  - name: LD_PRELOAD"
+echo "    value: /workspace/qwen3_6_scripts/cccl_preload_allocator.so"