feat: add gemm_n512_k2048 workloads and baseline solution

definitions/gemm/gemm_n512_k2048.json

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+{
+    "op_type": "gemm",
+    "tags": [
+        "model:qwen3.5-35b-a3b",
+        "status:reference",
+        "tp:2"
+    ],
+    "inputs": {
+        "A": {
+            "shape": [
+                "M",
+                "K"
+            ],
+            "dtype": "bfloat16"
+        },
+        "B": {
+            "shape": [
+                "N",
+                "K"
+            ],
+            "dtype": "bfloat16"
+        }
+    },
+    "outputs": {
+        "C": {
+            "shape": [
+                "M",
+                "N"
+            ],
+            "dtype": "bfloat16"
+        }
+    },
+    "reference": "import torch\n\ndef run(A, B):\n    C = torch.matmul(A, B.T)\n    return C",
+    "name": "gemm_n512_k2048",
+    "description": "General matrix multiply (GEMM) C = A @ B.T. Captured from Qwen3.5-35B-A3B at TP=2. N=512, K=2048.",
+    "axes": {
+        "M": {
+            "type": "var"
+        },
+        "N": {
+            "type": "const",
+            "value": 512
+        },
+        "K": {
+            "type": "const",
+            "value": 2048
+        }
+    }
+}

solutions/baseline/gemm/gemm_n512_k2048/torch_matmul_8b8ea6.json

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+{
+  "name": "torch_matmul_8b8ea6",
+  "definition": "gemm_n512_k2048",
+  "author": "PyTorch",
+  "spec": {
+    "language": "python",
+    "target_hardware": [
+      "NVIDIA_H100",
+      "NVIDIA_A100",
+      "CPU"
+    ],
+    "entry_point": "main.py::run",
+    "dependencies": [],
+    "destination_passing_style": false
+  },
+  "sources": [
+    {
+      "path": "main.py",
+      "content": "import torch\n\ndef run(A, B):\n    C = torch.matmul(A, B.T)\n    return C"
+    }
+  ],
+  "description": "Baseline GEMM implemented with torch.matmul."
+}

tests/references/test_gemm_n512_k2048.py

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+import torch
+
+
+def run(A, B):
+    M, K = A.shape
+    N, K2 = B.shape
+    assert K == K2
+    assert N == 512
+    assert K == 2048
+    C = torch.matmul(A, B.T)
+    return C
+
+
+def generate_random_inputs(M, N=512, K=2048, device="cuda"):
+    A = torch.randn(M, K, dtype=torch.bfloat16, device=device)
+    B = torch.randn(N, K, dtype=torch.bfloat16, device=device)
+    return {"A": A, "B": B}
+
+
+def test_correctness(M=32, atol=1e-2, rtol=1e-2):
+    print(f"\n{'='*60}")
+    print(f"Testing GEMM N=512, K=2048, M={M}")
+    print(f"{'='*60}")
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    if device == "cpu":
+        print("WARNING: CUDA not available, skipping test")
+        return True
+
+    inputs = generate_random_inputs(M, device=device)
+
+    ref_C = run(inputs["A"], inputs["B"])
+
+    A_f32 = inputs["A"].float()
+    B_f32 = inputs["B"].float()
+    expected = torch.matmul(A_f32, B_f32.T).to(torch.bfloat16)
+
+    abs_diff = torch.abs(ref_C.float() - expected.float())
+    max_abs_diff = abs_diff.max().item()
+    mean_abs_diff = abs_diff.mean().item()
+
+    print(f"Max absolute difference: {max_abs_diff:.6e}")
+    print(f"Mean absolute difference: {mean_abs_diff:.6e}")
+
+    close = torch.allclose(ref_C.float(), expected.float(), atol=atol, rtol=rtol)
+    if close:
+        print(f"\n✓ PASSED (atol={atol}, rtol={rtol})")
+    else:
+        print(f"\n✗ FAILED (atol={atol}, rtol={rtol})")
+    return close
+
+
+def main():
+    print("Testing GEMM N=512, K=2048 Reference Implementation")
+
+    test_configs = [1, 4, 16, 64, 256]
+    passed = 0
+    total = len(test_configs)
+
+    for M in test_configs:
+        try:
+            if test_correctness(M):
+                passed += 1
+        except Exception as e:
+            print(f"✗ Test failed with exception: {e}")
+            import traceback
+            traceback.print_exc()
+
+    print(f"\n{'='*60}")
+    print(f"Summary: {passed}/{total} tests passed")
+    print(f"{'='*60}")
+
+
+if __name__ == "__main__":
+    main()

workloads/gemm/gemm_n512_k2048.jsonl

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+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":8192},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"e3253003-7ed4-4c98-9b72-d9bf1bd31f2a"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":1111},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"f7670b35-e258-42dd-98e2-a1d00bc1f889"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":100},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"af5c5626-d798-42e3-ac09-ca6e16ca50d6"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":99},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"63601b13-03a1-46e1-9aef-fc7595645dfc"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":98},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"283ef5fb-8e7f-482f-9b49-e77d3709e53e"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":97},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"769d33ae-ded8-4864-95a9-8b0070957a8b"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":96},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"b38c420e-63bb-4ab2-8b86-bbe54b172fe2"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":51},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"956336e2-4d66-442c-a511-073f9b73036c"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":7962},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"ceb33a84-8854-4639-b937-45ffd736d091"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":113},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"f3518ee6-b062-4228-9806-e7f61e98875a"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":6016},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"4d841f31-5098-4be1-ad1e-850aa682c608"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":95},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"ea42c706-2b1e-43a2-a579-18e984f7cb11"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":219},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"7d88c702-a277-4aaa-a151-4cedc8e1ec1b"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":7794},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"8e242714-feb3-4d27-b10f-fb97dd6666cf"},"solution":null,"evaluation":null}
+{"definition":"gemm_n512_k2048","workload":{"axes":{"M":5574},"inputs":{"A":{"type":"random"},"B":{"type":"random"}},"uuid":"b7dd4d94-09a3-4fc3-a587-0f401c9ee350"},"solution":null,"evaluation":null}