The CS 6120 Course Blog

Loop Pipeline Initiation Interval Estimation Using LLVM

by Jie Liu


Loop pipelining is an important performance optimization technique that exploits the parallelism among loop iterations. In unoptimized loops written in sequential languages like C/C++, one iteration can only begin after the previous iteration is complete. Loop pipelining allows loop iterations to overlap, which better utilizes resources and improves the overall throughput. To implement loop pipelining in hardware, however, it usually takes non-trivial efforts for hardware-description languages (HDL) programmers to produce a cycle-accurate register-transfer-level (RTL) design. Moreover, once the RTL design is fixed, it is difficult to retarget various design points because the datapath and control logics are explicitly constructed in the implementation.

With high-level synthesis (HLS) emerging as an alternative design fashion, programmers can simply add a pragma in a C/C++ loop to enable loop pipelining in hardware. However, programmers at an early stage don't have much information on the performance of loops they write. If the specified pipeline initial interval (II) cannot be achieved, the HLS tools will throw out messages at least after running synthesis, which can take hours for a large design. In this project, I want to perform a quick analysis of the achievable loop pipeline II written at the C level. I believe early feedbacks will help programmers iterate their designs in a much faster way.


There are typically two factors of constraints that limit the degree of parallelism loop pipelining can exploit: one is hardware resource contention, and the other is the data dependencies between loop iterations.

We denote the lower-bound II limited by resource usage confliction as ResMII, which is given by the formula (1), according to this paper:

[ ResMII = \max_{r \in R} ResMII_r = \max_{r \in R} {\lceil \frac{O_r}{N_r} \rceil} ]

Where $R$ is the set of available resources, $O_r$ is the number of operations in the loop body which occupy the resource $r$, and $N_r$ is the number of allocated resources, for example, the number of memory ports, or the number of DSPs.

RecMII stands for Recurrence minimum II. It denotes the lower-bound of II due to loop-carried dependence. A loop-carried dependence indicates that operations in the subsequent loop iteration cannot start until the operations in the current iteration have finished. Array accesses are a common source of loop-carried dependence. I refered to this document and copied the following code snippet, which shows that the next iteration of the loop will read the array element updated by the current loop iteration. The minimum initiation interval, in this case, is the total number of clock cycles required for memory read, the add operation, and the memory write.

for (i = 1; i < N; i++)
    mem[i] = mem[i-1] + i;

The overall minimum initiation interval, considering both resource constraints and data dependences, is $MII = Max(ResMII, RecMII)$, which takes the maximum achievable II under both circumstances. We can take a look at a warm-up example:

int test()
  int A[20];
  for (unsigned t = 2; t <= 20 - 1; t++)
        A[t] = A[t - 2] + A[t - 1];
  return 0;

There are two load operations and one write operation in the loop body. If we assume the array A is mapped to a RAM with one read port and one write port, then we can calculate ResMII to be 2, as the number of memory read operations divided by the number of read ports. As for loop-carried dependency, we notice that the current loop iteration needs to read array elements computed by the previous loop (A[t - 1]) and the one before the previous loop (A[t - 2]), which means the operations in the current loop cannot get scheduled until A[t - 2] and A[t - 1] write back. If we assume that the memory read and write operations both take 1 clock cycle, and the add operation, since it is a combinational logic, can be merged with memory read into one clock cycle, then the RecMII can be estimated as 2. The overall minimum initiation interval is $max(ResMII, RecMII) = 2$.


I implemented an LLVM pass to analyze the innermost loops. The problem is decomposed into two parts: estimate resource-constrained ResMII and data-dependency bounded RecMII.

For ResMII, it is easy to estimate using the formula (1). The pass traverses the loop, calculates the number of independent loads and stores in the code, and divide it by the number of available resources. The parameters of available resources come from HLS resource constraints for real application needs, e.g., mapping arrays to 1R1W RAMs or dual-port RAMs, or registers, which are configured in the tool by the user. To use the pass for estimation, the users need to manually set them as MACROs. I applied LLVM global value numbering pass on input programs before my handwritten pass, in order to avoid counting recurring memory accesses. The pass I implemented only considered the memory resource constraints. There are other types of resources that can incur usage conflict, for instance, the number of I/O ports, and certain compute resources, which I did not take into account for the course of simplicity.

The biggest challenge of this project is to estimate the RecMII, which basically requires precise loop-carried dependency analysis. Ideally, I can use the maximum dependency distance as an approximate of the RecMII. LLVM has a loop dependency analysis pass. It successfully extracted the dependency information in simple test programs, such as the warm-up example above, but for some realistic benchmarks, it fails to figure out the dependence distance. In this case, to restore the estimated data-dependency bounded II, I computed the index difference of memory read-after-write (RAW) dependence pairs as a rough approximation. Since the actual RecMII depends on the number of cycles between memory read and write operations in the real schedule, the rough estimation performed in this pass can deviate from the real numbers. Additionally, the estimation is tuned towards a specific HLS tool, which is Catapult HLS tool in this project. I observed that if the loop body contains merely simple arithmetic operations on memory loads, and a single write-back to the memory, which is the case for all the tested benchmarks in PolyBench, the load-to-store operations scheduled in Catapult HLS tool are usually back-to-back and take 2 cycles. Therefore, by injecting into some tool-specific observation knowledge, the estimation of RecMII is able to be close to real numbers given by the tool.


I extracted 4 kernels in PolyBench that contain loop-carried dependencies. Memory accesses in PolyBench kernels are all affine accesses, which simplifies our analysis on the memory access index. The baseline numbers are given by running the key kernel code in Catapult HLS tool.

BenchmarksCatapult HLSMy LLVM Pass
jacobi-1dResMII = 3, RecMII = 1ResMII = 3, RecMII = 1
jacobi-2dResMII = 5, RecMII = 1ResMII = 5, RecMII = 1
heat-3dResMII = 7, RecMII = 1ResMII = 7, RecMII = 1
seidel-2dResMII = 9, RecMII = 3ResMII = 9, RecMII = 2

The experiment results show that the estimation of ResMII is precise for all test cases. The RecMII given by our analysis pass is a good estimation for most benchmarks, except seidel-2d. Actually, for all benchmarks shown except seidel-2d, there is no real loop-carried dependency, and for this reason the pass returns RecMII=1. Seidel-2d has a memory RAW dependency, and the dependency distance cannot be identified by LLVM loop dependency analysis pass. Therefore, for this benchmark, the pass computes the index difference between the dependency load-store pair, which is 1, and adds the tool-specific additional schedule cycles to it, which outputs 2. It turns out that the estimation is over ideal, and the real II given by Catapult HLS tool is 3.

Future work