Fully Homomorphic Encryption (FHE) is increasingly adopted in Online Analytical Processing (OLAP) systems to protect against data breaches. However, existing FHE-based OLAP systems must sequentially execute computationally intensive homomorphic comparisons for query processing on FHE ciphertexts, leading to significantly lower performance compared to traditional OLAP systems. We present Opera, the first high-performance FHE-based OLAP system on the GPU. Observing the redundancy in re-executing homomorphic comparisons from scratch, we design Homcache to create a GPU-accelerated parallel query execution workflow: Opera selectively caches comparison results and allows subsequent comparisons to reuse them, thereby reducing the computational cost per comparison and facilitating concurrent execution of multiple comparisons on the GPU. Nevertheless, due to the large size of FHE ciphertexts, Homcache can grow substantially, and naively applying traditional plaintext-oriented cache management strategies like LRU results in suboptimal performance. To ensure stable high performance, we develop a density-driven algorithm tailored for managing ciphertexts in Homcache. Compared to notable baselines running on CPUs, Opera reduces query latency by up to 9612x with 1.2GB cache storage without compromising security. OPERA's source code, complete benchmark suite, and raw results are available at github.com/hku-systems/Opera.