diff --git a/src/cmdtools/analysis/pcca.py b/src/cmdtools/analysis/pcca.py
index 5f9afdd..19fe5b5 100644
--- a/src/cmdtools/analysis/pcca.py
+++ b/src/cmdtools/analysis/pcca.py
@@ -128,8 +128,7 @@ def krylovschur(A, n, massmatrix=None, onseperation="continue"):
 
     from petsc4py import PETSc
     from slepc4py import SLEPc
-    M = PETSc.Mat().create()
-    M.createDense(list(np.shape(A)), array=A)
+    M = petsc_matrix(A)
     E = SLEPc.EPS().create()
     E.setOperators(M)
     E.setDimensions(nev=n)
@@ -137,3 +136,17 @@ def krylovschur(A, n, massmatrix=None, onseperation="continue"):
     E.solve()
     X = np.column_stack([x.array for x in E.getInvariantSubspace()])
     return X[:, :n]
+
+
+def petsc_matrix(A):
+    from scipy import sparse
+    from petsc4py import PETSc
+    from slepc4py import SLEPc
+
+    M = PETSc.Mat()
+    if sparse.isspmatrix_csr(A):
+        nrows = np.size(A, 0)
+        M.createAIJWithArrays(nrows, (A.indptr, A.indices, A.data))
+    else:
+        M.createDense(list(np.shape(A)), array=A)
+    return M
diff --git a/tests/test_pcca.py b/tests/test_pcca.py
index d844b1f..f74fd36 100644
--- a/tests/test_pcca.py
+++ b/tests/test_pcca.py
@@ -51,22 +51,48 @@ def test_krylovschur(n=30, m=5, N=100):
             assert R == m
 
 
-def test_bench_scipyschur(benchmark, n=500, m=6):
+N_BENCHMARK = 1000
+M_BENCHMARK = 10
+
+
+def test_bench_scipyschur(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
     T = utils.randompropagator(n)
     massmatrix = np.diag(np.ones(n))
     solver = pcca.ScipySchur()
     benchmark(solver.solve, T, m, massmatrix)
 
 
-def test_bench_scipyqz(benchmark, n=500, m=6):
+def test_bench_scipyqz(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
     T = utils.randompropagator(n)
     solver = pcca.ScipySchur()
     benchmark(solver.solve, T, m)
 
 
-def test_bench_krylovschur(benchmark, n=500, m=6):
+def test_bench_krylovschur(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
     if not pcca.HAS_SLEPC:
         return
     T = utils.randompropagator(n)
     solver = pcca.KrylovSchur(onseperation="continue")
     benchmark(solver.solve, T, m)
+
+
+def test_bench_krylovschursparse_dense(benchmark, n=N_BENCHMARK, m=M_BENCHMARK):
+    if not pcca.HAS_SLEPC:
+        return
+    from scipy import sparse
+    T = utils.randompropagator(n)
+    T = sparse.csr_matrix(T)
+    solver = pcca.KrylovSchur(onseperation="continue")
+    benchmark(solver.solve, T, m)
+
+
+def test_bench_krylovschursparse_sparse(benchmark, n=N_BENCHMARK, m=M_BENCHMARK, p=0.01):
+    if not pcca.HAS_SLEPC:
+        return
+    from scipy import sparse
+    T = sparse.random(n, n, p)
+    T = np.array(np.identity(n) + T)
+    T = utils.rowstochastic(T)
+    T = sparse.csr_matrix(T)
+    solver = pcca.KrylovSchur(onseperation="continue")
+    benchmark(solver.solve, T, m)