### Table 2. Benchmarks sequential implementation

"... In PAGE 10: ...inear algebra, as described, e.g., in [17]. Table2 depicts the running times for the complete factorization of polynomials of growing degree. For each polynomial, this table also contains the time needed for the rst execution of steps 15 and 18 of the algorithm (i.... ..."

Cited by 1

### Table 2. Comparison of parallel and sequential implementations.

1996

"... In PAGE 3: ...That margins for improvements are smaller when better strategies are employed is conflrmed if we compare the efiective runtimes of the Chakrabarti{Yelick implementation on a 20 processor CM-5y, with the implementation by Sawada et al. (1994) on a PIM/m with 256 nodes, and with our own sequential implementation in C++ (Attardi and Traverso, 1995) on a single SparcStation5, as shown in Table2 (times are in seconds). Sawada et al.... ..."

Cited by 9

### TABLE 2: PACE2 sequential implementation.

1989

Cited by 5

### Table 2: Complexity of the sequential implementation. Resource Type Number of Resources

"... In PAGE 4: ... When Np gt; 1, another adder is required for the outer sum. Table2 summarizes the complexity of this simple implementation. Table 2: Complexity of the sequential implementation.... ..."

### Table 1 contains the processing requirements for a sequential implementation.

"... In PAGE 8: ... This is well below the required 2 seconds per page. Illustrative for the power of massively parallel computing is the speedup compared to the sequen- tial implementation as shown in Table1 . Although the processing capacity of each PE is much lower than a von Neumann processor, the number of processors working in parallel yield large speedups.... ..."

### Table 1 contains the processing requirements for a sequential implementation.

"... In PAGE 7: ... This is well below the required 2 seconds per page. Illustrative for the power of massively parallel computing is the speedup compared to the sequen- tial implementation as shown in Table1 . Although the processing capacity of each PE is much lower than a von Neumann processor, the number of processors working in parallel yield large speedups.... ..."

### Table 3. Comparison of sequential vs. parallel implementation

"... In PAGE 10: ... All benchmarks were obtained using a Pentium III processor rated at 450 Mhz, with the exception of the degree 1024000 polynomial, which was factored using a Pentium III processor rated at 500 Mhz. In Table3 , the performance of the sequential implementation of the factoriza- tion algorithm is compared with a parallel implementation. In this parallel imple- mentation, the computation of the two polynomial multiplications in the Cantor... ..."

Cited by 1

### Table 1: Algorithms currently implemented in the sequential BGL.

"... In PAGE 2: ... This parameterization allows extensive customization of the BGL, from storing user-defined data types with the vertices and edges of a graph to completely replacing the Parallel BGL graph types with application-specific data structures, without incurring additional overhead. Table1 lists some of the algorithms that are currently implemented in the BGL. Following our philosophy of software libraries and reuse, we have recently developed the Parallel Boost Graph Library (Parallel BGL) [21] on top of the sequential BGL.... ..."

### Table 4. Surface rendering timing comparison for CAVASS (sequential implementation with and without antialiasing) and surface rendering as implemented in VTK.

"... In PAGE 8: ... Results for sequential surface rendering and parallel and sequential volume rendering appear in Tables 4 and 5, respectively. Table4 shows that sequential CAVASS shell rendering, entirely in software and without antialiasing, was more than 8.5 times faster than hardware-based rendering as implemented in VTK for the largest dataset (super) in our test.... ..."